From 589764dd653fda31f1aa85de3263fcc985ca18fe Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 25 Jul 2023 21:46:09 +0800 Subject: [PATCH 01/62] 1. add debug_app.cpp with my modified 2. ppo.py with clip output 3. raisim_gym_helper.py got a Logger using logging which could save your log in both .log and console 4. design my self Environment.hpp and could read configuration to simplify your training with less compilation --- examples/CMakeLists.txt | 3 +- examples/src/maps/anymals.cpp | 146 ++++++- raisimGymTorch/CMakeLists.txt | 2 + .../raisimGymTorch/algo/ppo/module.py | 2 + raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py | 1 + .../raisimGymTorch/env/debug_app.cpp | 78 +++- .../env/envs/rsg_anymal/Environment.hpp | 2 +- .../envs/rsg_anymal_modified/Environment.hpp | 379 ++++++++++++++++++ .../env/envs/rsg_anymal_modified/cfg.yaml | 32 ++ .../env/envs/rsg_anymal_modified/runner.py | 195 +++++++++ .../env/envs/rsg_anymal_modified/tester.py | 76 ++++ .../helper/raisim_gym_helper.py | 27 +- rsc/anymal_c/urdf/anymal.urdf | 16 +- 13 files changed, 927 insertions(+), 32 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/runner.py create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/tester.py diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 2d578b77b..87dde6f72 100755 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -4,7 +4,7 @@ project(raisim_examples LANGUAGES CXX) #== # Dependencies #== - +#set(raisim /home/lr-2002/code/raisimLib/raisim/linux/lib/cmake/raisim/) find_package(raisim CONFIG REQUIRED) find_package(Eigen3 REQUIRED HINTS ${Eigen3_HINT}) @@ -77,6 +77,7 @@ create_executable(deletion src/server/deletion.cpp) create_executable(inverseDynamics src/server/inverseDynamics.cpp) create_executable(dzhanibekovEffect src/server/dzhanibekovEffect.cpp) create_executable(go1 src/server/go1.cpp) +create_executable(leg src/server/leg.cpp) # xml reader create_executable(xmlRader src/xml/xmlReader.cpp) diff --git a/examples/src/maps/anymals.cpp b/examples/src/maps/anymals.cpp index daddc496d..d4e48882e 100755 --- a/examples/src/maps/anymals.cpp +++ b/examples/src/maps/anymals.cpp @@ -2,6 +2,88 @@ // Inc. prior to usage. #include "raisim/RaisimServer.hpp" +#include +#include +#define PI 3.1415926 +int cnt1=0, cnt2=0; +void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +{ +// std::cout<< "size is "<< angle_list.size() << std::endl; + double base1= 0.4, base3=0.03; + double base2 = -2 * base1; + double ang = abs(sin( float(idx) / T * PI)) * rate; + double ang2 = ang * 1.2; + if (idx % int(T) == 1) cnt1++; + int idx_base = 0; +// std::cout<> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); + std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } + std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} int main(int argc, char* argv[]) { auto binaryPath = raisim::Path::setFromArgv(argv[0]); @@ -10,12 +92,11 @@ int main(int argc, char* argv[]) { /// create raisim world raisim::World world; - world.setTimeStep(0.001); + world.setTimeStep(0.01); /// create objects auto ground = world.addGround(0, "gnd"); ground->setAppearance("hidden"); - auto anymalB = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\anymal\\urdf\\anymal.urdf"); auto anymalC = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\anymal_c\\urdf\\anymal.urdf"); /// anymalC joint PD controller @@ -24,21 +105,33 @@ int main(int argc, char* argv[]) { jointVelocityTarget.setZero(); Eigen::VectorXd jointPgain(anymalC->getDOF()), jointDgain(anymalC->getDOF()); - jointPgain.tail(12).setConstant(100.0); - jointDgain.tail(12).setConstant(1.0); + jointPgain.tail(12).setConstant(200); + jointDgain.tail(12).setConstant(4); +// jointDgain[7] = 2; +// jointDgain[10] = 2; +// jointDgain[13] = 2; +// jointDgain[16] = 2; + +// std::vector footIndices_; +// +// footIndices_.push_back(anymalC->getFrameIdxByName("LF_SHANK")); +// footIndices_.push_back(anymalC->getFrameIdxByName("RF_SHANK")); +// footIndices_.push_back(anymalC->getFrameIdxByName("LH_SHANK")); +// footIndices_.push_back(anymalC->getFrameIdxByName("RH_SHANK")); + +// auto footframeindex = anymalC->getFrameIdxByName("foot") anymalC->setGeneralizedCoordinate(jointNominalConfig); anymalC->setGeneralizedForce(Eigen::VectorXd::Zero(anymalC->getDOF())); anymalC->setPdGains(jointPgain, jointDgain); - anymalC->setPdTarget(jointNominalConfig, jointVelocityTarget); anymalC->setName("anymalC"); - +// std::vector> foot_list; +// for(auto i=0; igetFramePosition(footIndices_[i], foot_list[i]); +// std::cout<<"posi " << foot_list[i] <setGeneralizedCoordinate(jointNominalConfig); - anymalB->setGeneralizedForce(Eigen::VectorXd::Zero(anymalB->getDOF())); - anymalB->setPdGains(jointPgain, jointDgain); - anymalB->setPdTarget(jointNominalConfig, jointVelocityTarget); - anymalB->setName("anymalB"); /// friction example. uncomment it to see the effect // anymalB->getCollisionBody("LF_FOOT/0").setMaterial("LF_FOOT"); @@ -53,23 +146,36 @@ int main(int argc, char* argv[]) { /// graphs std::vector jointNames = {"LF_HAA", "LF_HFE", "LF_KFE", "RF_HAA", "RF_HFE", "RF_KFE", "LH_HAA", "LH_HFE", "LH_KFE", "RH_HAA", "RH_HFE", "RH_KFE"}; - auto jcGraph = server.addTimeSeriesGraph("joint position", jointNames, "time", "position"); - auto jvGraph = server.addTimeSeriesGraph("joint velocity", jointNames, "time", "velocity"); - auto jfGraph = server.addTimeSeriesGraph("joint torque", jointNames, "time", "torque"); raisim::VecDyn jc(12), jv(12), jf(12); + Eigen::VectorXd angle_list(12); + Eigen::Vector3d pose(0, 0, 3); +// Eigen::Vector2d join; + auto join = anymalC->getJointLimits(); + Eigen::VectorXd limit_list; + Eigen::VectorXd gene_angle; + gene_angle.setZero(12); + map_from_origin_to_limit(join, limit_list, gene_angle); + std::cout<setBasePos(pose); + angle_generator(angle_list, i, 30.f, 0.3); + + jointNominalConfig.tail(12) = angle_list; if (i % 10 == 0) { - jc = anymalC->getGeneralizedCoordinate().e().tail(12); - jv = anymalC->getGeneralizedVelocity().e().tail(12); - jf = anymalC->getGeneralizedForce().e().tail(12); - jcGraph->addDataPoints(world.getWorldTime(), jc); - jvGraph->addDataPoints(world.getWorldTime(), jv); - jfGraph->addDataPoints(world.getWorldTime(), jf); + } + anymalC->setPdTarget(jointNominalConfig, jointVelocityTarget); +// anymalC->setBasePos_e(pose); +// anymalC->setGeneralizedCoordinate(jointNominalConfig); } server.killServer(); diff --git a/raisimGymTorch/CMakeLists.txt b/raisimGymTorch/CMakeLists.txt index cc212d42d..8c9901416 100755 --- a/raisimGymTorch/CMakeLists.txt +++ b/raisimGymTorch/CMakeLists.txt @@ -108,3 +108,5 @@ FOREACH(subdir ${SUBDIRS}) endif() endif() ENDFOREACH() +add_executable(t_sin ../examples/src/maps/t_sin.cpp) +target_include_directories(t_sin PUBLIC ${EIGEN3_INCLUDE_DIRS} ) diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/module.py b/raisimGymTorch/raisimGymTorch/algo/ppo/module.py index 2885f2d7a..6d931b4b5 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/module.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/module.py @@ -18,6 +18,8 @@ def __init__(self, architecture, distribution, device='cpu'): def sample(self, obs): self.action_mean = self.architecture.architecture(obs).cpu().numpy() actions, log_prob = self.distribution.sample(self.action_mean) + # actions = (actions - actions.min()) / (actions.max() - actions.min()) * np.pi + actions = np.clip(actions, -1, 1) return actions, log_prob def evaluate(self, obs, actions): diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py index 885f73e11..0cad97548 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py @@ -77,6 +77,7 @@ def act(self, actor_obs): self.actor_obs = actor_obs with torch.no_grad(): self.actions, self.actions_log_prob = self.actor.sample(torch.from_numpy(actor_obs).to(self.device)) + return self.actions def step(self, value_obs, rews, dones): diff --git a/raisimGymTorch/raisimGymTorch/env/debug_app.cpp b/raisimGymTorch/raisimGymTorch/env/debug_app.cpp index 88fd18fb1..58410fe12 100755 --- a/raisimGymTorch/raisimGymTorch/env/debug_app.cpp +++ b/raisimGymTorch/raisimGymTorch/env/debug_app.cpp @@ -9,6 +9,59 @@ int THREAD_COUNT = 1; using namespace raisim; +#define PI 3.1415926 +//void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +//{ +//// std::cout<< "size is "<< angle_list.size() << std::endl; +// double base1= 0.4, base2 = -0.8, base3=0.03; +// double ang = abs(sin( float(idx) / T * PI)) * rate; +// +// int idx_base = 0; +//// std::cout<(), 1); EigenBoolVec dones(config["num_envs"].template As(), 1); action.setZero(); +// for(auto i:action.rows()) + std::cout< ob_ref(observation), action_ref(action); Eigen::Ref reward_ref(reward); Eigen::Ref dones_ref(dones); +// std::cout<getGeneralizedForce().squaredNorm()); - rewards_.record("forwardVel", std::min(4.0, bodyLinearVel_[0])); + rewards_.record("forwardVel", -bodyLinearVel_[0]); return rewards_.sum(); } diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp new file mode 100644 index 000000000..b5c5cc0a9 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp @@ -0,0 +1,379 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#define PI 3.1415926 + +double cal(double low, double upp, double now){ + return (now + 1)/2 * (upp- low) + low; +} + +void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); +// std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } +// std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} + +void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +{ +// std::cout<< "size is "<< angle_list.size() << std::endl; + double base1= 0.4, base2 = -0.8, base3=0.0; + double ang = abs(sin( float(idx) / T * PI)) * rate; + + int idx_base = 0; +// std::cout<(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + /// add objects + anymal_ = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_->setName("dog"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); + nJoints_ = gvDim_ - 6; + + /// initialize containers + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + /// this is nominal configuration of anymal +// gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// anymal_1->setGeneralizedCoordinate(gc_init_); + gc_init_ << 0, 0, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; + + /// set pd gains + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(200.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(4); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + + /// MUST BE DONE FOR ALL ENVIRONMENTS + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + +// READ_YAML(); + actionStd_.setConstant(action_std); + + join_limit = anymal_->getJointLimits(); + /// Reward coefficients + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + /// indices of links that should not make contact with ground + footIndices_.insert(anymal_->getBodyIdx("LF_SHANK")); + footIndices_.insert(anymal_->getBodyIdx("RF_SHANK")); + footIndices_.insert(anymal_->getBodyIdx("LH_SHANK")); + footIndices_.insert(anymal_->getBodyIdx("RH_SHANK")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + void init() final { } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + /// action scaling + COUNT ++; +// std::cout<setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = 0; quat[1] = 0; quat[2] = 0; quat[3] = 0; +// raisim::quatToRotMat(quat, rot); +// anymal_->setBaseOrientation(rot); +// raisim::Vec<4> quat(4); +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// anymal_->setBaseOrientation(quat); +// pTarget_.tail(nJoints_) = angle_list; +// anymal_->setPdTarget(pTarget_, vTarget_); + } + // angle_list *= 0.3; +// angle_list.setZero(); + Eigen::VectorXd idx1(8), idx2(4); + idx1.setZero();idx2.setZero(); + idx1<<1, 2, 4, 5, 7,8,10,11; + idx2<<0, 3, 6, 9; + + pTarget12_ = action.cast(); + Eigen::VectorXd ttmp =action.cast(); + map_from_origin_to_limit(join_limit, pTarget12_,ttmp); + // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); +// pTarget12_ += actionMean_; +// pTarget12_ = angle_list; + + + angle_mulit(pTarget12_, idx1, action_std); + angle_mulit(pTarget12_, idx2, action_std); + + + angle_list_for_work = angle_list * for_work_rate; + + + + + pTarget12_ = angle_list_for_work + pTarget12_; +// pTarget12_[11] = pTarget12_[10] *-2; +// pTarget12_[8] = pTarget12_[7] * -2; +// pTarget12_[1] = -pTarget12_[10]; +// pTarget12_[2] = -pTarget12_[11]; +// pTarget12_[4] = -pTarget12_[7]; +// pTarget12_[5] = -pTarget12_[8]; +// pTarget12_[9] = -0.03; +// pTarget12_[0] = -pTarget12_[9]; +// pTarget12_[6] = 0.03; +// pTarget12_[3] = -pTarget12_[6]; + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + + + + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } + for(int i=0; i<=1; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } + rrr += abs(gc_[5] - gc_init_[5]) * 2 * stable_reward_rate; +// if(COUNT != 0) +// { +// rrr += (gc_ - gc_init_).norm() * reference_rate *0.1; +// } + + +// rrr +=(gc_init_.head(7) - gc_.head(7)).norm() * stable_reward_rate; + rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; +// rrr += abs(bodyLinearVel_[1]); +// rrr += abs(bodyLinearVel_[2]); +// rrr += abs(bodyLinearVel_[0]); + +// rrr += abs(gc_[2] - gc_init_[2]) ; + +// rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::mi); +rewards_.record("Stable", 1 - rrr, false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); + + obDouble_ << double(COUNT)/schedule_T, + rot.e().row(2).transpose(), /// body orientation + bodyLinearVel_, bodyAngularVel_, /// body linear&angular velocity + gc_.tail(12); /// joint angles + gv_.tail(12); /// joint velocity + } + + void observe(Eigen::Ref ob) final { + /// convert it to float + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + + /// if the contact body is not feet + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// std::cout<<"terminate "<0.3 ) + { + return true; + } +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) +// return true; + +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8 || ) +// return true; + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. They are placed to show you how to create a random number sampler. + bool float_base; + std::normal_distribution normDist_; + thread_local static std::mt19937 gen_; +}; +thread_local std::mt19937 raisim::ENVIRONMENT::gen_; + +} + +//int main() +//{ +// raisim::ENVIRONMENT a("../rsc/","/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml", +// true); +//} \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml new file mode 100644 index 000000000..5f0f14b7b --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml @@ -0,0 +1,32 @@ +seed: 1 +record_video: False + +environment: + render: True +# just testing commenting + num_envs: 100 + eval_every_n: 40 + num_threads: 30 + simulation_dt: 0.0025 + control_dt: 0.01 + max_time: 4.0 + action_std: 0.0035 + show_ref: False + angle_rate: 0.4 + stable: 5 + reference : 0 + schedule: 40 + for_work: 1 + float_base: False + learnning_rate: 5e-4 + reward: + forwardVel: + coeff: 0.3 + torque: + coeff: -4e-5 + Stable: + coeff: 0.9 + +architecture: + policy_net: [128, 128] + value_net: [128, 128] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/runner.py new file mode 100755 index 000000000..70f5637c0 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/runner.py @@ -0,0 +1,195 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger +from raisimGymTorch.env.bin.rsg_anymal_modified import NormalSampler +from raisimGymTorch.env.bin.rsg_anymal_modified import RaisimGymEnv +from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer +import os +import math +import time +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse + +# task specification +task_name = "anymal_locomotion" + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=120) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +mode = args.mode +weight_path = args.weight + +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# create environment from the configuration file +env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts +num_threads = cfg['environment']['num_threads'] + +# Training +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * env.num_envs + +avg_rewards = [] + +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + env.num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + +saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, + save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) +logger = RaisimLogger(saver.data_dir+"/train.log") + +if mode =='train' or mode == 'retrain': + tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update + +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=4, + device=device, + log_dir=saver.data_dir, + shuffle_batch=False, + learning_rate=cfg['environment']['learnning_rate'] + ) + +reward_analyzer = RewardAnalyzer(env, ppo.writer) +biggest_reward = 0 +biggest_iter = 0 +if mode == 'retrain': + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +# if load_best == True: +# weight_path = "" +print = logger.info + +total_update = args.update +if mode =='train' or mode == 'retrain': + for update in range(total_update): + start = time.time() + env.reset() + reward_sum = 0 + done_sum = 0 + average_dones = 0. + + if update % cfg['environment']['eval_every_n'] == 0: + print("Visualizing and evaluating the current policy") + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + # we create another graph just to demonstrate the save/load method + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + + env.turn_on_visualization() + env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') + + for step in range(n_steps): + with torch.no_grad(): + frame_start = time.time() + obs = env.observe(False) + # action = ppo.act(torch.from_numpy(obs).cpu()) + action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + # print(action.min()) + reward, dones = env.step(action.cpu().detach().numpy()) + reward_analyzer.add_reward_info(env.get_reward_info()) + frame_end = time.time() + wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) + if wait_time > 0.: + time.sleep(wait_time) + + env.stop_video_recording() + env.turn_off_visualization() + + reward_analyzer.analyze_and_plot(update) + env.reset() + env.save_scaling(saver.data_dir, str(update)) + + # actual training + for step in range(n_steps): + obs = env.observe() + action = ppo.act(obs) + reward, dones = env.step(action) + ppo.step(value_obs=obs, rews=reward, dones=dones) + done_sum = done_sum + np.sum(dones) + reward_sum = reward_sum + np.sum(reward) + # take st step to get value obs + obs = env.observe() + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) + average_ll_performance = reward_sum / total_steps + if average_ll_performance > biggest_reward: + biggest_iter = update + biggest_reward = average_ll_performance + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + env.save_scaling(saver.data_dir, str(update)) + average_dones = done_sum / total_steps + avg_rewards.append(average_ll_performance) + + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) + + # curriculum update. Implement it in Environment.hpp + env.curriculum_callback() + + end = time.time() + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) + print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) + print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) + print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) + * cfg['environment']['control_dt']))) + print('----------------------------------------------------\n') +else: + env.reset() + start = time.time() + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + for step in range(n_steps * 10): + time.sleep(0.01) + obs = env.observe() + action = ppo.act(obs) + reward, dones = env.step(action) + +print(f'biggest:{biggest_reward},rate = {biggest_iter}') \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/tester.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/tester.py new file mode 100755 index 000000000..bdea58d30 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/tester.py @@ -0,0 +1,76 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.bin import rsg_anymal +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +import raisimGymTorch.algo.ppo.module as ppo_module +import os +import math +import time +import torch +import argparse + + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-w', '--weight', help='trained weight path', type=str, default='') +args = parser.parse_args() + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# create environment from the configuration file +cfg['environment']['num_envs'] = 1 + +env = VecEnv(rsg_anymal.RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts + +weight_path = args.weight +iteration_number = weight_path.rsplit('/', 1)[1].split('_', 1)[1].rsplit('.', 1)[0] +weight_dir = weight_path.rsplit('/', 1)[0] + '/' + +if weight_path == "": + print("Can't find trained weight, please provide a trained weight with --weight switch\n") +else: + print("Loaded weight from {}\n".format(weight_path)) + start = time.time() + env.reset() + reward_ll_sum = 0 + done_sum = 0 + average_dones = 0. + n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) + total_steps = n_steps * 1 + start_step_id = 0 + + print("Visualizing and evaluating the policy: ", weight_path) + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], torch.nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(weight_path)['actor_architecture_state_dict']) + + env.load_scaling(weight_dir, int(iteration_number)) + env.turn_on_visualization() + + # max_steps = 1000000 + max_steps = 1000 ## 10 secs + + for step in range(max_steps): + time.sleep(0.01) + obs = env.observe(False) + action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + reward_ll, dones = env.step(action_ll.cpu().detach().numpy()) + reward_ll_sum = reward_ll_sum + reward_ll[0] + if dones or step == max_steps - 1: + print('----------------------------------------------------') + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(reward_ll_sum / (step + 1 - start_step_id)))) + print('{:<40} {:>6}'.format("time elapsed [sec]: ", '{:6.4f}'.format((step + 1 - start_step_id) * 0.01))) + print('----------------------------------------------------\n') + start_step_id = step + 1 + reward_ll_sum = 0.0 + + env.turn_off_visualization() + env.reset() + print("Finished at the maximum visualization steps") diff --git a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py index bc65f3500..123cd1fca 100755 --- a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py +++ b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py @@ -3,7 +3,7 @@ import os import ntpath import torch - +import logging class ConfigurationSaver: def __init__(self, log_dir, save_items): @@ -58,3 +58,28 @@ def load_param(weight_path, env, actor, critic, optimizer, data_dir): actor.distribution.load_state_dict(checkpoint['actor_distribution_state_dict']) critic.architecture.load_state_dict(checkpoint['critic_architecture_state_dict']) optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + +class RaisimLogger(): + def __init__(self, path): + self.logger = logging.getLogger(__file__) + self.logger.setLevel(logging.INFO) + self.fh = logging.FileHandler(path) + self.ch = logging.StreamHandler() + self.fh.setLevel(logging.INFO) + self.ch.setLevel(logging.INFO) + + self.formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S") + self.ch.setFormatter(self.formatter) + self.fh.setFormatter(self.formatter) + self.logger.addHandler(self.ch) + self.logger.addHandler(self.fh) + + def info(self, msg): + self.logger.info(msg) + def warning(self, msg): + self.logger.warning(msg) + def debug(self, msg): + self.logger.debug(msg) + def error(self, msg): + self.logger.error(msg) \ No newline at end of file diff --git a/rsc/anymal_c/urdf/anymal.urdf b/rsc/anymal_c/urdf/anymal.urdf index 230fb2bf3..58e8f74ac 100755 --- a/rsc/anymal_c/urdf/anymal.urdf +++ b/rsc/anymal_c/urdf/anymal.urdf @@ -689,7 +689,7 @@ - + @@ -765,7 +765,7 @@ - + @@ -933,7 +933,7 @@ - + @@ -1009,7 +1009,7 @@ - + @@ -1177,7 +1177,7 @@ - + @@ -1253,7 +1253,7 @@ - + @@ -1421,7 +1421,7 @@ - + @@ -1497,7 +1497,7 @@ - + From 346d58ec69cbfe5be4f4d581ace53953298b0bc9 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 25 Jul 2023 21:49:59 +0800 Subject: [PATCH 02/62] 1. add debug_app.cpp with my modified 2. ppo.py with clip output 3. raisim_gym_helper.py got a Logger using logging which could save your log in both .log and console 4. design my self Environment.hpp and could read configuration to simplify your training with less compilation --- .gitignore | 3 +- .../env/envs/rsg_leg/Environment.hpp | 159 ++++++++++++++++++ .../raisimGymTorch/env/envs/rsg_leg/cfg.yaml | 24 +++ .../raisimGymTorch/env/envs/rsg_leg/runner.py | 147 ++++++++++++++++ 4 files changed, 332 insertions(+), 1 deletion(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/Environment.hpp create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/cfg.yaml create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/runner.py diff --git a/.gitignore b/.gitignore index a88a8c81c..e10542adb 100755 --- a/.gitignore +++ b/.gitignore @@ -19,4 +19,5 @@ examples/cmake-build*/* */data/* **/activation.raisim data/* -**/.DS_Store \ No newline at end of file +**/.DS_Store +/raisimGymTorch/raisimGymTorch/algo/pre_sin.py diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/Environment.hpp new file mode 100644 index 000000000..1ed569acb --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/Environment.hpp @@ -0,0 +1,159 @@ +#pragma once +#include +#include +#include +#include "../../RaisimGymEnv.hpp" +#include +namespace raisim{ + +class ENVIRONMENT: public RaisimGymEnv{ +public: + explicit ENVIRONMENT(const std::string& resourceDir, const Yaml::Node& cfg, bool visualizable): + RaisimGymEnv(resourceDir, cfg), visualizable_(visualizable){ + world_ = std::make_unique(); + + leg_ = world_->addArticulatedSystem(resourceDir_ + "do_legs/do_legs.urdf") ; // + leg_->setName("leg"); + leg_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_ -> addGround(); + + gcDim_ = leg_ -> getGeneralizedCoordinateDim(); + gvDim_ = leg_ -> getDOF(); +// nJoints_ = gvDim_ - 6;// + nJoints_ = gcDim_; + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_);pTarget_.setZero(nJoints_); + no_collision_.push_back(1); + no_collision_.push_back(2); + no_collision_.push_back(3); +// no_collision_.push_back(); + + gc_init_ << 0, 0, 0, 0, 0; // + + + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(100.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(1); + leg_ ->setPdGains(jointPgain, jointDgain); + leg_ ->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + obDim_ = 10;// + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + + actionMean_ = gc_init_.tail(nJoints_); + double action_std; + READ_YAML(double, action_std, cfg_["action_std"]); + actionStd_.setConstant(action_std); + + rewards_.initializeFromConfigurationFile(cfg["reward"]); + + +// footIndices_.insert(leg_ ->getBodyIdx("shank")); // set for foot; +// footIndices_.insert(leg_ ->getBodyIdx("knee")); // set for foot; +// footIndices_.insert(leg_ ->getBodyIdx("thigh")); // set for foot; +// footIndices_.insert(leg_ ->getBodyIdx("body")); // set for foot; +// footIndices_.insert(leg_ ->getBodyIdx("lk5")); // set for foot; + + + if(visualizable_) + { + server_ = std::make_unique(world_.get()); + server_ ->launchServer(); + server_ ->focusOn(leg_); + } + } + + + + void init() final {} + + void reset() final { + leg_ ->setState(gc_init_, gv_init_); + updateObservation(); + } + + float step(const Eigen::Ref& action) final{ +// std::cout<< "action:\n" << action << std::endl; + pTarget12_ = action.cast (); // target for p ,12 is because of the 12 joints + pTarget12_ = pTarget12_.cwiseProduct(actionStd_); + pTarget12_ += actionMean_; + pTarget_.tail(nJoints_) = pTarget12_; +// pTarget_[1] = 0; +// std::cout<setPdTarget(pTarget_, vTarget_); + // note to excute the step command + // the pd is just the controller to fit the position to the robot + for(int i=0; i lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_ -> unlockVisualizationServerMutex(); + } + updateObservation(); + +// rewards_.record("torque", leg_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::min(3.0, bodyLinearVel_[0])); + rewards_.record("position", -bodyLinearPos_[1]); // +// std::cout<< "height : " << std::endl<< -bodyLinearPos_[1] << std::endl; + return rewards_.sum(); + } + + void updateObservation() { + leg_ ->getState(gc_, gv_); +// raisim::Vec<4> quat; +// raisim::Mat<3, 3> rot; +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// raisim::quatToRotMat(quat, rot); +// bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); +// bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// std::cout << "data: " << leg_ ->getGeneralizedVelocity().data() << std::endl; + + +// obDouble_ << gc_[2], // body height // 1 +// rot.e().row(2).transpose(), // body orientation // 3 +// gc_.tail(12), // joint angles // 12 +// bodyLinearVel_, bodyAngularVel_, // body vel // 6 +// gv_.tail(12); // 12 + bodyLinearVel_ = gv_; + bodyLinearPos_ = gc_; + obDouble_ << gc_.tail(5), gv_.tail(5); + } + + void observe(Eigen::Ref ob) final{ + ob = obDouble_.cast( ); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + for(auto& contact: leg_->getContacts()) + { + std::vector::iterator it = std::find(no_collision_.begin(), no_collision_.end(), contact.getlocalBodyIndex()); + if(it != no_collision_.end()) // if it != end ,means it was found + return true; + } + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() {}; + + +private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* leg_; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_, bodyLinearPos_; + std::set footIndices_; + std::vector no_collision_; + +}; + + +} \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/cfg.yaml new file mode 100755 index 000000000..d8666d266 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/cfg.yaml @@ -0,0 +1,24 @@ +seed: 1 +record_video: yes + +environment: + render: True +# just testing commenting + num_envs: 100 + eval_every_n: 300 + num_threads: 30 + simulation_dt: 0.0025 + control_dt: 0.01 + max_time: 4.0 + action_std: 0.3 + reward: + forwardVel: + coeff: 0.3 + torque: + coeff: -4e-5 + position: + coeff: 0.4 + +architecture: + policy_net: [128, 128] + value_net: [128, 128] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/runner.py new file mode 100644 index 000000000..1207ac1d3 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_leg/runner.py @@ -0,0 +1,147 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher +from raisimGymTorch.env.bin.rsg_leg import NormalSampler +from raisimGymTorch.env.bin.rsg_leg import RaisimGymEnv +import os +import math +import time +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse + +task_name = 'leg_locomotion' + +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train' ) +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +args = parser.parse_args() +mode = args.mode +weight_path = args.weight + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + '/../../../../..' + +cfg = YAML().load(open(task_path + '/cfg.yaml', 'r')) + +env = VecEnv(RaisimGymEnv(home_path + '/rsc/', dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +ob_dim = env.num_obs +act_dim = env.num_acts +num_threads = cfg['environment']['num_threads'] + +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * env.num_envs + +avg_rewards = [] + +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + env.num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) + +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + + +saver = ConfigurationSaver(log_dir=home_path + '/raisimGymTorch/data/' + task_name, + save_items=[task_path+'/cfg.yaml', task_path+'/Environment.hpp']) +tensorboard_launcher(saver.data_dir + '/..') + + +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=4, + learning_rate=1e-2, + device=device, + log_dir=saver.data_dir, + shuffle_batch=False) + +if mode == 'retrain': + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + + +for update in range(10000): + start = time.time() + env.reset() + reward_ll_sum = 0 + done_sum = 0 + average_dones = 0 + + if update % cfg['environment']['eval_every_n'] == 0: + print('Visualizing and evaluating the current policy') + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'ppo_optimizer_state_dict': ppo.optimizer.state_dict() + }, saver.data_dir+'/full_'+ str(update)+'.pt') + + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(saver.data_dir+'/full_'+ str(update) + '.pt')['actor_architecture_state_dict']) + + env.turn_on_visualization() + env.start_video_recording(datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S') + 'policy_' + str(update) + '.mp4') + + for step in range(n_steps * 2): + with torch.no_grad(): + frame_start = time.time() + obs = env.observe(False) + action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + reward_ll, dones =env.step(action_ll.cpu().detach().numpy()) + frame_end = time.time() + wait_time = cfg['environment']['control_dt'] - (frame_end - frame_start) + if wait_time> 0: + time.sleep(wait_time) + + env.stop_video_recording() + env.turn_off_visualization() + + env.reset() + env.save_scaling(saver.data_dir, str(update)) + + for step in range(n_steps): + obs = env.observe() + action = ppo.act(obs) + reward, dones = env.step(action) + ppo.step(value_obs=obs, rews=reward, dones=dones) + done_sum = done_sum + np.sum(dones) + reward_ll_sum = reward_ll_sum + np.sum(reward) + + obs = env.observe() + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 ==0, update=update) + average_ll_performance = reward_ll_sum / total_steps + average_dones = done_sum / total_steps + avg_rewards.append(average_ll_performance) + + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(5) * 0.2).to(device)) # + + env.curriculum_callback() + + end = time.time() + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) + print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) + print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) + print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) + * cfg['environment']['control_dt']))) + print('----------------------------------------------------\n') From fd0703a9860cce07e0f4eb4523c6f46fa4cd808e Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 25 Jul 2023 21:51:24 +0800 Subject: [PATCH 03/62] 1. add debug_app.cpp with my modified 2. ppo.py with clip output 3. raisim_gym_helper.py got a Logger using logging which could save your log in both .log and console 4. design my self Environment.hpp and could read configuration to simplify your training with less compilation --- examples/src/server/leg.cpp | 73 +++++ rsc/do_legs/do_legs.urdf | 228 ++++++++++++++++ rsc/leg/a.urdf | 512 ++++++++++++++++++++++++++++++++++++ rsc/leg/leg.urdf | 131 +++++++++ rsc/leg/leg1.urdf | 224 ++++++++++++++++ rsc/leg/leg2.urdf | 128 +++++++++ 6 files changed, 1296 insertions(+) create mode 100644 examples/src/server/leg.cpp create mode 100644 rsc/do_legs/do_legs.urdf create mode 100644 rsc/leg/a.urdf create mode 100644 rsc/leg/leg.urdf create mode 100644 rsc/leg/leg1.urdf create mode 100644 rsc/leg/leg2.urdf diff --git a/examples/src/server/leg.cpp b/examples/src/server/leg.cpp new file mode 100644 index 000000000..d94ab7b6f --- /dev/null +++ b/examples/src/server/leg.cpp @@ -0,0 +1,73 @@ +// +// Created by lr-2002 on 23-7-14. +// +#include "raisim/World.hpp" +#include "raisim/RaisimServer.hpp" +float calculate_angle(float ang) +{ + return ang * 3.14 / 180; +} +void print_collision(raisim::Sphere* sphere) +{ + + auto& contacts = sphere->getContacts(); + for (auto i : contacts) + { + std::cout<< i.getPosition() << std::endl; + } +} +int main(int argc, char * argv[]) +{ + auto binaryPath = raisim::Path::setFromArgv(argv[0]); + raisim::World::setActivationKey(binaryPath.getDirectory() + "\\rsc\\activation.raisim"); + + // create raisim world + raisim::World world; + world.setTimeStep(0.01); + auto ground = world.addGround(); + + world.setMaterialPairProp("steel", "steel", 0.8, 0.95, 0.001); + // launch server + + raisim::RaisimServer server(&world); + server.launchServer(); + + auto sphere = world.addSphere(0.4, 0.3, "steel"); + sphere->setName("sphere"); + auto leg = world.addArticulatedSystem(binaryPath.getDirectory()+"\\rsc\\leg\\leg2.urdf"); + leg->setName("leg"); + +// Eigen::VectorXd gc(3); +// gc << 0, 0, 0; +// leg->setGeneralizedCoordinate(gc); + + +// Eigen::VectorXd gc(3); +// Eigen::VectorXd gv(3); +// gc << calculate_angle(-50), 0, calculate_angle(-50); +// gv << 0, -1, -1; +// sphere->setPosition(0, 0, 0.4); +// int cnt = 0; +// auto foot_index = leg->getBodyIdx("r_2"); + while(1) + { +// cnt ++ ; +// gc << calculate_angle(-50 + 0.8* cnt), 0, calculate_angle(-90); +// print_collision(sphere); +// std::cout<setGeneralizedCoordinate(gc); +// leg->setGeneralizedVelocity(gv); +// leg->setGeneralizedForce(gc); + server.integrateWorldThreadSafe(); + raisim::MSLEEP(10); +// for(auto& contact : leg->getContacts()) +// { +// if(contact.getPairContactIndexInPairObject() != raisim::BodyType::STATIC) +// { +// world.getObject(contact.getPairObjectIndex())->getContacts(); +// } +// } + } + + server.killServer(); +} \ No newline at end of file diff --git a/rsc/do_legs/do_legs.urdf b/rsc/do_legs/do_legs.urdf new file mode 100644 index 000000000..cdab0daf2 --- /dev/null +++ b/rsc/do_legs/do_legs.urdf @@ -0,0 +1,228 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + > + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/rsc/leg/a.urdf b/rsc/leg/a.urdf new file mode 100644 index 000000000..9f5b7d53f --- /dev/null +++ b/rsc/leg/a.urdf @@ -0,0 +1,512 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/rsc/leg/leg.urdf b/rsc/leg/leg.urdf new file mode 100644 index 000000000..a1fb77105 --- /dev/null +++ b/rsc/leg/leg.urdf @@ -0,0 +1,131 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +> + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/rsc/leg/leg1.urdf b/rsc/leg/leg1.urdf new file mode 100644 index 000000000..04a3cdc5b --- /dev/null +++ b/rsc/leg/leg1.urdf @@ -0,0 +1,224 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/rsc/leg/leg2.urdf b/rsc/leg/leg2.urdf new file mode 100644 index 000000000..5165e82c8 --- /dev/null +++ b/rsc/leg/leg2.urdf @@ -0,0 +1,128 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 3944d5c9129aea124c9febcf616cbf56218ef432 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 25 Jul 2023 22:48:08 +0800 Subject: [PATCH 04/62] write Readme_zh.md --- Readme_zh.md | 114 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 114 insertions(+) create mode 100644 Readme_zh.md diff --git a/Readme_zh.md b/Readme_zh.md new file mode 100644 index 000000000..d6d6bb73c --- /dev/null +++ b/Readme_zh.md @@ -0,0 +1,114 @@ +# Raisim 中文使用指南 + +## 写在前面 + +相信大家都是因为自己老师的要求，过来学习Raisim，或者之前也已经尝试过一些别的项目（issac,gazebo,webots）之类的。 + +首先，我想说一下，为什么要使用Raisim + +1. `C++` + `Python`，这个软件对`python`还是比较友好的，但是为了提高你的使用体验，你需要有一定的`C++`基础，尤其是强化学习部分，你需要自己动手修改`Environment.hpp` 如果不太理解`C++`的面对对象的概念，以及一些`STL`的基本结构（`iterator`之类的），使用起来会有一些难度 +2. `Linux， Win, Mac` 友好。目前为止我只尝试了`win` 和`linux`，这两个系统的支持度还都是挺好的，尤其是`linux`，对视频的录制很好用，不需要一直盯着屏幕。 +3. 强化学习很快，由于代码完全开放，相较于`Unity`而言，你的自由度更高，你可以很轻松的添加你自己的东西。 + +其次，我说一下本版本相较于作者的原版本的区别 + +1. 添加了更相信的`log`系统，在`gym`文件夹的`helper`里添加了`logging`系统，使用起来能够在对应的`saver.dir`中生成名为`train.log`的文件，方便后期查询自己的日志 +2. 训练过程中自动保存最优的模型，后期会修改把之前的弱鸡模型给删除 +3. 完善了`test`模块，在`gymtorch`文件夹中，你的`runner`可以直接通过运行`python yourfilepath(imply it use your path)/runner.py --mode test --weight yourweightpath`在`unity`等渲染引擎中展示你的模型 + +## 怎么使用 + +### 复制项目 + +1. `fork`本`repo`到你个人的`github`中（右上角有一个`fork`按钮） +2. 在你自己的`fork`版本中，选择`code`按钮，复制`https`链接 +3. 在你本地打开`powershell`(windows)或者是`base/zsh`(linux)，然后`git clone your-repo-http`（不清楚的自行`google`） +4. 切换到你的目标位置，通常是`raisimLib` + +### 编译项目 + +编译是`C/C++`在执行之前都需要进行的事情，简单来说，就是把做饭需要的菜都放到桌子上，具体执行方式如下 + +#### 安装依赖 + +我下边只列出来你需要安装的软件，如果有不会的请自行`google` + +1. `cmake` +2. `Eigen` （`C++`需要用到） +3. 编译器 + 1. Linux: `gnu` + 2. Windows: `Visual Studio`（注意不要安装错误了，是紫色的VS，不是蓝色的VSC） + +#### 设置路径 + +##### Windows + +自行`google`: 如何将路径添加到环境变量 + +##### Linux + +自行`google`: 如何将路径添加到`Linux`路径 + +#### checklist（检查你需要添加的路径） + +1. `cmake`:添加的路径下需要有`cmake.exe`文件 + +2. `eigen`:添加的路径需要有`Dense.hpp`文件 +3. `WORKSPACE`:这个变量指向的是`RaisimLib`的位置，这个路径在windows中需要单独列出来，在环境变量中直接新建，而不是通过`path`建立 +4. `LOCAL_INSTALL`：这个变量指向的是你希望在那里部署你的库文件， 建议直接在`raisimLib`下新建文件夹，命名为`raisim_build`，然后指向这个位置即可 + +#### 开始部署/编译 + +1. 让你的终端切换到`raisimlib`文件夹 +2. 创建`build`文件夹，并切换进去 +3. 参考[官方文档](https://raisim.com/sections/Installation.html#building-raisim-examples-and-installing-raisimpy)的建议，运行对应的命令，如果你不想编译`python`，就把`-DRAISIM_PY=ON`删掉（他的`python`好像作者自己都不喜欢用） + +### 激活文件 + +1. [点这里申请你的激活文件](https://docs.google.com/forms/d/e/1FAIpQLScNL0vbZPDNS93L6Jv6fgR51WTsvXxfhnVOtKDVRdAmHIoG4w/viewform?usp=sf_link) + 1. 写自己的信息 + 2. 通过提供的`google drive`获取软件，读取你的机器码 + 3. 将机器码复制进去，然后申请`license`就可以了（他们会给你的邮箱发一个`activation.raisim`） +2. 在你的根目录(`linux`就是`~/`，`windows`是`c:users/xxx（你的账户）/`)，创建一个文件夹，命名为`.raisim`，将`activation.raisim`复制进去即可。 + +### 运行例子 + +#### Linux + +生成的`example`就在你创建的`build`文件夹下，进去就可以找到类似`alingo`之类的二进制文件，你需要通过`chmod +X ./alingo` 运行即可 + +#### Windows + +如果你是通过VS建立的项目，这个路径通常在`RasimLib/raisim/win32/bin`里，会有一大堆的`exe`，点击运行即可 + + + + + +# `RaisimGymTorch` + +如果你是要学习`raisimgymmtorch`进行强化学习，那就需要理解以下几个基本概念 + +1. `setup.py`(`raisimlib/raisimgymtorch/`) 对所有的环境进行编译，生成在python中可以使用的库 +2. `Environment.hpp` 这个文件定义了你的机器人与环境作用的方式 + 1. 执行你的`action` + 2. 计算`reward` + 3. 更新`observation` +3. `runner.py` 这个文件是你的`python`的主框架 + 1. 记录`log` + 2. 更新`observation` + 3. 记录`state-action`对 + 4. `ppo`计算 + 5. 生成`action` + 6. 更新`model` +4. `cfg.yaml` 定义超参数 + 1. `num_envs`：子空间的数量（开多空间） + 2. `eval_per_iters`：执行多少个`iters`之后会记录并展示 + + + + + +## 欢迎PR + +有问题发`Issue`，觉得ok的话，点一个`star`，tks! \ No newline at end of file From 9462bb8db60d3d8c226de17b6bdbc70d88e42d3c Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 25 Jul 2023 22:54:03 +0800 Subject: [PATCH 05/62] write Readme_zh.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index f6dc5e34c..7d40787c9 100755 --- a/README.md +++ b/README.md @@ -6,6 +6,9 @@ Click to watch the video Documentation available on the [RaiSim Tech website](http://raisim.com). +Click here to read chinese version [中文简体](./Readme_zh.md) + + ## New update! RaisimUnreal (Alpha version) is now released. You can get it [here](https://github.com/raisimTech/raisimLib/releases). From 98f6e76db4faa8537c44860b6845d37858da8596 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 25 Jul 2023 22:57:59 +0800 Subject: [PATCH 06/62] write Readme_zh.md --- Readme_zh.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Readme_zh.md b/Readme_zh.md index d6d6bb73c..8c078706d 100644 --- a/Readme_zh.md +++ b/Readme_zh.md @@ -12,9 +12,9 @@ 其次，我说一下本版本相较于作者的原版本的区别 -1. 添加了更相信的`log`系统，在`gym`文件夹的`helper`里添加了`logging`系统，使用起来能够在对应的`saver.dir`中生成名为`train.log`的文件，方便后期查询自己的日志 +1. 添加了更详细的`log`系统，在`gym`文件夹的`helper`里添加了`logging`系统，使用起来能够在对应的`saver.dir`中生成名为`train.log`的文件，方便后期查询自己的日志 2. 训练过程中自动保存最优的模型，后期会修改把之前的弱鸡模型给删除 -3. 完善了`test`模块，在`gymtorch`文件夹中，你的`runner`可以直接通过运行`python yourfilepath(imply it use your path)/runner.py --mode test --weight yourweightpath`在`unity`等渲染引擎中展示你的模型 +3. 完善了`runner.py --mode test`模块，在`gymtorch`文件夹中，你的`runner`可以直接通过运行`python yourfilepath(imply it use your path)/runner.py --mode test --weight yourweightpath`在`unity`等渲染引擎中展示你的模型 ## 怎么使用 From 2ae74b6dba31664e2514e6fac87fb1c2b02e94d8 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 1 Aug 2023 13:53:39 +0800 Subject: [PATCH 07/62] add rsg_go1 --- examples/src/server/go1.cpp | 84 +++- .../raisimGymTorch/env/auto_runner.py | 26 ++ .../envs/rsg_anymal_modified/Environment.hpp | 3 +- .../env/envs/rsg_go1/Environment.hpp | 433 ++++++++++++++++++ .../raisimGymTorch/env/envs/rsg_go1/cfg.yaml | 32 ++ .../raisimGymTorch/env/envs/rsg_go1/runner.py | 204 +++++++++ .../raisimGymTorch/env/envs/rsg_go1/tester.py | 76 +++ 7 files changed, 850 insertions(+), 8 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/auto_runner.py create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/tester.py diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 2d5ebffa5..82374d562 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -3,41 +3,111 @@ #include "raisim/RaisimServer.hpp" #include "raisim/World.hpp" +#define PI 3.1415926 +void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +{ + // todo ang2, rate, ang +// std::cout<< "size is "<< angle_list.size() << std::endl; + double base1= 0.8, base3=0.0; + double base2 = -2 * base1; + double ang = -abs(sin( float(idx) / T * PI)) * rate; + double ang2 = -0.15 * ang; + double ang3 = ang2 * 1.2; + int idx_base = 0; +// std::cout<getGeneralizedCoordinateDim()), jointVelocityTarget(go1->getDOF()); - jointNominalConfig << 0, 0, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; + jointNominalConfig << 0, 0, 0.30, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; + +// jointNominalConfig.tail(12).setZero(); + Eigen::VectorXd tmp(12); + tmp.setZero(); + angle_generator(tmp, 0, 80.f); + jointNominalConfig.tail(12) = tmp; jointVelocityTarget.setZero(); Eigen::VectorXd jointPgain(go1->getDOF()), jointDgain(go1->getDOF()); - jointPgain.tail(12).setConstant(10.0); - jointDgain.tail(12).setConstant(0.1); + jointPgain.tail(12).setConstant(120.0); + jointDgain.tail(12).setConstant(3); go1->setGeneralizedCoordinate(jointNominalConfig); go1->setGeneralizedForce(Eigen::VectorXd::Zero(go1->getDOF())); go1->setPdGains(jointPgain, jointDgain); go1->setPdTarget(jointNominalConfig, jointVelocityTarget); go1->setName("go1"); - + std::cout<< go1->getGeneralizedCoordinate(); /// launch raisim server raisim::RaisimServer server(&world); server.focusOn(go1); server.launchServer(); + Eigen::Vector3d pos(0,0, 3); for (int i=0; i<2000000; i++) { - RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) + + RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) + +// sleep(1); + go1->setBasePos(pos); + angle_generator(tmp, i, 30.f, 0.6); +// double rrr = 0; +// for(int i=4; i<=6 ; i++) +// { +// rrr += abs(jointNominalConfig[i] - ) * stable_reward_rate ; +// } + jointNominalConfig.tail(12) = tmp; + go1->setPdTarget(jointNominalConfig, jointVelocityTarget); + + server.integrateWorldThreadSafe(); + } server.killServer(); diff --git a/raisimGymTorch/raisimGymTorch/env/auto_runner.py b/raisimGymTorch/raisimGymTorch/env/auto_runner.py new file mode 100644 index 000000000..aff020b07 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/auto_runner.py @@ -0,0 +1,26 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +import os + +# file = open('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml') +def change_data(path, flag, data): + y = YAML().load(open(path, 'r')) + for a,b in zip(flag, data): + y['environment'][a] = b + # wr = yaml.dump(y) + print(y) + with open(path, 'w') as f : + dump(y, f, Dumper=RoundTripDumper) + +test_list = [x/1000 for x in range(0,20,2)] +test_T = [x for x in range(30, 70, 10)] +# print(test_list) +length = 300 +for j in test_T: + for i in test_list: + change_data('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml',['action_std','schedule'], [i, j]) + if i == 0: + continue + else: + os.system(f'python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py -u {length} |grep biggest' ) + +# os.system('python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py -u 3 |grep biggest' ) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp index b5c5cc0a9..37e8cf921 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/Environment.hpp @@ -110,7 +110,7 @@ class ENVIRONMENT : public RaisimGymEnv { READ_YAML(bool, float_base, cfg_["float_base"]); READ_YAML(float,schedule_T, cfg_["schedule"]); /// add objects - anymal_ = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_ = world_->addArticulatedSystem(resourceDir_+"/go1/go1.urdf"); anymal_->setName("dog"); anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); world_->addGround(); @@ -293,6 +293,7 @@ class ENVIRONMENT : public RaisimGymEnv { // rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); // rewards_.record("forwardVel", std::mi); rewards_.record("Stable", 1 - rrr, false); +rewards_.record("forwardVel", bodyLinearVel_[0], false); gc_old = gc_.tail(12); ref_old = angle_list; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp new file mode 100644 index 000000000..81a9d7751 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp @@ -0,0 +1,433 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#define PI 3.1415926 + +double cal(double low, double upp, double now){ + return (now + 1)/2 * (upp- low) + low; +} + +void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); +// std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } +// std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} + +//void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +//{ +//// std::cout<< "size is "<< angle_list.size() << std::endl; +// double base1= 0.82, base3=0.0; +// double base2 = -2 * base1; +// double ang = abs(sin( float(idx) / T * PI)) * rate; +// +// int idx_base = 0; +//// std::cout<(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + /// add objects + anymal_ = world_->addArticulatedSystem(resourceDir_+"/a1/urdf/a1.urdf"); + anymal_->setName("dog"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); + nJoints_ = gvDim_ - 6; + + /// initialize containers + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + /// this is nominal configuration of anymal +// gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// anymal_1->setGeneralizedCoordinate(gc_init_); + gc_init_<< 0, 0, 0.30, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; + +// jointNominalConfig.tail(12).setZero(); + Eigen::VectorXd tmp(12); + tmp.setZero(); + angle_generator(tmp, 0, 80.f); + gc_init_.tail(12) = tmp; + + /// set pd gains + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(120.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(3); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + + /// MUST BE DONE FOR ALL ENVIRONMENTS + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + +// READ_YAML(); + actionStd_.setConstant(action_std); + + join_limit = anymal_->getJointLimits(); + /// Reward coefficients + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + /// indices of links that should not make contact with ground + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + void init() final { } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + /// action scaling + COUNT ++; +// std::cout<setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = 0; quat[1] = 0; quat[2] = 0; quat[3] = 0; +// raisim::quatToRotMat(quat, rot); +// anymal_->setBaseOrientation(rot); +// raisim::Vec<4> quat(4); +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// anymal_->setBaseOrientation(quat); +// pTarget_.tail(nJoints_) = angle_list; +// anymal_->setPdTarget(pTarget_, vTarget_); + } + // angle_list *= 0.3; +// angle_list.setZero(); + Eigen::VectorXd idx1(8), idx2(4); + idx1.setZero();idx2.setZero(); + idx1<<1, 2, 4, 5, 7,8,10,11; + idx2<<0, 3, 6, 9; + + pTarget12_ = action.cast(); + Eigen::VectorXd ttmp =action.cast(); + map_from_origin_to_limit(join_limit, pTarget12_,ttmp); + // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); +// pTarget12_ += actionMean_; +// pTarget12_ = angle_list; + + + angle_mulit(pTarget12_, idx1, action_std); + angle_mulit(pTarget12_, idx2, action_std); + + + angle_list_for_work = angle_list * for_work_rate; + + + + + pTarget12_ = angle_list_for_work + pTarget12_; +// pTarget12_[11] = pTarget12_[10] *-2; +// pTarget12_[8] = pTarget12_[7] * -2; +// pTarget12_[1] = -pTarget12_[10]; +// pTarget12_[2] = -pTarget12_[11]; +// pTarget12_[4] = -pTarget12_[7]; +// pTarget12_[5] = -pTarget12_[8]; +// pTarget12_[9] = -0.03; +// pTarget12_[0] = -pTarget12_[9]; +// pTarget12_[6] = 0.03; +// pTarget12_[3] = -pTarget12_[6]; + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + + + + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } +// rrr *= float(COUNT )/ schedule_T); +// for(int i=0; i<=1; i++) +// { +// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; +// } +// rrr += abs(gc_[5] - gc_init_[5]) * 2 * stable_reward_rate; +// if(COUNT != 0) +// { +// rrr += (gc_ - gc_init_).norm() * reference_rate *0.1; +// } + + +// rrr +=(gc_init_.head(7) - gc_.head(7)).norm() * stable_reward_rate; + rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; +// rrr += abs(bodyLinearVel_[1]); +// rrr += abs(bodyLinearVel_[2]); +// rrr += abs(bodyLinearVel_[0]); + +// rrr += abs(gc_[2] - gc_init_[2]) ; + +// rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::mi); + rewards_.record("Stable", 1 - rrr, false); + rewards_.record("forwardVel", bodyLinearVel_[0], false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// std::cout<< rot.e().row(2).transpose().size() << " " < ob) final { + /// convert it to float + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + +// / if the contact body is not feet + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// std::cout<<"terminate "<0.3 ) +// { +// return true; +// } +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) +// return true; + +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8 || ) +// return true; + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. They are placed to show you how to create a random number sampler. + bool float_base; + std::normal_distribution normDist_; + thread_local static std::mt19937 gen_; +}; +thread_local std::mt19937 raisim::ENVIRONMENT::gen_; + +} + +//int main() +//{ +// raisim::ENVIRONMENT a("../rsc/","/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml", +// true); +//} \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml new file mode 100644 index 000000000..32a0b24c9 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml @@ -0,0 +1,32 @@ +seed: 1 +record_video: false + +environment: + render: true +# just testing commenting + num_envs: 100 + eval_every_n: 100 + num_threads: 30 + simulation_dt: 0.0025 + control_dt: 0.01 + max_time: 8.0 + action_std: 0.018 + show_ref: false + angle_rate: 0.3 + stable: 1 + reference: 0 + schedule: 60 + for_work: 1 + float_base: false + learnning_rate: 5e-4 + reward: + forwardVel: + coeff: 3 + torque: + coeff: -4e-5 + Stable: + coeff: 0.9 + +architecture: + policy_net: [128, 128] + value_net: [128, 128] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py new file mode 100755 index 000000000..7cf3010aa --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py @@ -0,0 +1,204 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger +from raisimGymTorch.env.bin.rsg_go1 import NormalSampler +from raisimGymTorch.env.bin.rsg_go1 import RaisimGymEnv +from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO + + +import os +import math +import time +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse + + + +# task specification +task_name = "go1_locomotion" + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=120) +parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +mode = args.mode +weight_path = args.weight +cfg_path = args.cfg_path +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +if cfg_path is not None: + cfg = YAML().load(open(cfg_path, 'r')) +else: + cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# create environment from the configuration file +env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts +num_threads = cfg['environment']['num_threads'] + +# Training +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * env.num_envs + +avg_rewards = [] + +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + env.num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + +saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, + save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) +logger = RaisimLogger(saver.data_dir+"/train.log") + +if mode =='train' or mode == 'retrain': + tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update + +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=4, + device=device, + log_dir=saver.data_dir, + shuffle_batch=False, + learning_rate=cfg['environment']['learnning_rate'] + ) + +reward_analyzer = RewardAnalyzer(env, ppo.writer) +biggest_reward = 0 +biggest_iter = 0 +if mode == 'retrain': + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +# if load_best == True: +# weight_path = "" +print = logger.info + +total_update = args.update +if mode =='train' or mode == 'retrain': + for update in range(total_update): + start = time.time() + env.reset() + reward_sum = 0 + done_sum = 0 + average_dones = 0. + + if update % cfg['environment']['eval_every_n'] == 0: + print("Visualizing and evaluating the current policy") + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + # we create another graph just to demonstrate the save/load method + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + + env.turn_on_visualization() + env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') + + for step in range(n_steps): + with torch.no_grad(): + frame_start = time.time() + obs = env.observe(False) + + # action = ppo.act(torch.from_numpy(obs).cpu()) + action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + # print(action.min()) + reward, dones = env.step(action.cpu().detach().numpy()) + reward_analyzer.add_reward_info(env.get_reward_info()) + frame_end = time.time() + wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) + if wait_time > 0.: + time.sleep(wait_time) + + env.stop_video_recording() + env.turn_off_visualization() + + reward_analyzer.analyze_and_plot(update) + env.reset() + env.save_scaling(saver.data_dir, str(update)) + + # actual training + for step in range(n_steps): + obs = env.observe() + action = ppo.act(obs) + reward, dones = env.step(action) + ppo.step(value_obs=obs, rews=reward, dones=dones) + done_sum = done_sum + np.sum(dones) + reward_sum = reward_sum + np.sum(reward) + # take st step to get value obs + obs = env.observe() + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) + average_ll_performance = reward_sum / total_steps + if average_ll_performance > biggest_reward: + biggest_iter = update + biggest_reward = average_ll_performance + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + env.save_scaling(saver.data_dir, str(update)) + average_dones = done_sum / total_steps + avg_rewards.append(average_ll_performance) + + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) + + # curriculum update. Implement it in Environment.hpp + env.curriculum_callback() + + end = time.time() + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) + print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) + print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) + print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) + * cfg['environment']['control_dt']))) + print('----------------------------------------------------\n') +else: + env.reset() + start = time.time() + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + for step in range(n_steps * 10): + time.sleep(0.01) + obs = env.observe() + action = ppo.act(obs) + reward, dones = env.step(action) + +print(f'biggest:{biggest_reward},rate = {biggest_iter}') \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/tester.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/tester.py new file mode 100755 index 000000000..bdea58d30 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/tester.py @@ -0,0 +1,76 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.bin import rsg_anymal +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +import raisimGymTorch.algo.ppo.module as ppo_module +import os +import math +import time +import torch +import argparse + + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-w', '--weight', help='trained weight path', type=str, default='') +args = parser.parse_args() + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# create environment from the configuration file +cfg['environment']['num_envs'] = 1 + +env = VecEnv(rsg_anymal.RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts + +weight_path = args.weight +iteration_number = weight_path.rsplit('/', 1)[1].split('_', 1)[1].rsplit('.', 1)[0] +weight_dir = weight_path.rsplit('/', 1)[0] + '/' + +if weight_path == "": + print("Can't find trained weight, please provide a trained weight with --weight switch\n") +else: + print("Loaded weight from {}\n".format(weight_path)) + start = time.time() + env.reset() + reward_ll_sum = 0 + done_sum = 0 + average_dones = 0. + n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) + total_steps = n_steps * 1 + start_step_id = 0 + + print("Visualizing and evaluating the policy: ", weight_path) + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], torch.nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(weight_path)['actor_architecture_state_dict']) + + env.load_scaling(weight_dir, int(iteration_number)) + env.turn_on_visualization() + + # max_steps = 1000000 + max_steps = 1000 ## 10 secs + + for step in range(max_steps): + time.sleep(0.01) + obs = env.observe(False) + action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + reward_ll, dones = env.step(action_ll.cpu().detach().numpy()) + reward_ll_sum = reward_ll_sum + reward_ll[0] + if dones or step == max_steps - 1: + print('----------------------------------------------------') + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(reward_ll_sum / (step + 1 - start_step_id)))) + print('{:<40} {:>6}'.format("time elapsed [sec]: ", '{:6.4f}'.format((step + 1 - start_step_id) * 0.01))) + print('----------------------------------------------------\n') + start_step_id = step + 1 + reward_ll_sum = 0.0 + + env.turn_off_visualization() + env.reset() + print("Finished at the maximum visualization steps") From 4836ff764c72e810a6f0270ed905a1f10fc56175 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 1 Aug 2023 13:56:16 +0800 Subject: [PATCH 08/62] add deploy part --- .gitignore | 1 + .../env/deploy/data/Environment.hpp | 387 ++++++++++++++++++ .../env/deploy/data/full_152.pt | Bin 0 -> 538687 bytes .../env/deploy/data/mean152.csv | 34 ++ .../raisimGymTorch/env/deploy/robot.py | 44 ++ .../env/envs/rsg_anymal_modified/cfg.yaml | 2 +- .../helper/raisim_gym_helper.py | 2 +- 7 files changed, 468 insertions(+), 2 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/data/Environment.hpp create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/data/full_152.pt create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/data/mean152.csv create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/robot.py diff --git a/.gitignore b/.gitignore index e10542adb..f6ac31f3d 100755 --- a/.gitignore +++ b/.gitignore @@ -21,3 +21,4 @@ examples/cmake-build*/* data/* **/.DS_Store /raisimGymTorch/raisimGymTorch/algo/pre_sin.py +/raisimGymTorch/raisimGymTorch/env/deploy/data/ diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/data/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/deploy/data/Environment.hpp new file mode 100644 index 000000000..7e9f5b5c4 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/deploy/data/Environment.hpp @@ -0,0 +1,387 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#define PI 3.1415926 + +double cal(double low, double upp, double now){ + return (now + 1)/2 * (upp- low) + low; +} + +void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); +// std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } +// std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} + +void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +{ +// std::cout<< "size is "<< angle_list.size() << std::endl; + double base1= 0.82, base3=0.0; + double base2 = -2 * base1; + double ang = abs(sin( float(idx) / T * PI)) * rate; + + int idx_base = 0; +// std::cout<(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + /// add objects + anymal_ = world_->addArticulatedSystem(resourceDir_+"/a1/urdf/a1.urdf"); + anymal_->setName("dog"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); + nJoints_ = gvDim_ - 6; + + /// initialize containers + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + /// this is nominal configuration of anymal +// gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// anymal_1->setGeneralizedCoordinate(gc_init_); + gc_init_<< 0, 0, 0.30, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; + +// jointNominalConfig.tail(12).setZero(); + Eigen::VectorXd tmp(12); + tmp.setZero(); + angle_generator(tmp, 0, 80.f); + gc_init_.tail(12) = tmp; + + /// set pd gains + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(120.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(3); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + + /// MUST BE DONE FOR ALL ENVIRONMENTS + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + +// READ_YAML(); + actionStd_.setConstant(action_std); + + join_limit = anymal_->getJointLimits(); + /// Reward coefficients + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + /// indices of links that should not make contact with ground + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + void init() final { } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + /// action scaling + COUNT ++; +// std::cout<setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = 0; quat[1] = 0; quat[2] = 0; quat[3] = 0; +// raisim::quatToRotMat(quat, rot); +// anymal_->setBaseOrientation(rot); +// raisim::Vec<4> quat(4); +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// anymal_->setBaseOrientation(quat); +// pTarget_.tail(nJoints_) = angle_list; +// anymal_->setPdTarget(pTarget_, vTarget_); + } + // angle_list *= 0.3; +// angle_list.setZero(); + Eigen::VectorXd idx1(8), idx2(4); + idx1.setZero();idx2.setZero(); + idx1<<1, 2, 4, 5, 7,8,10,11; + idx2<<0, 3, 6, 9; + + pTarget12_ = action.cast(); + Eigen::VectorXd ttmp =action.cast(); + map_from_origin_to_limit(join_limit, pTarget12_,ttmp); + // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); +// pTarget12_ += actionMean_; +// pTarget12_ = angle_list; + + + angle_mulit(pTarget12_, idx1, action_std); + angle_mulit(pTarget12_, idx2, action_std); + + + angle_list_for_work = angle_list * for_work_rate; + + + + + pTarget12_ = angle_list_for_work + pTarget12_; +// pTarget12_[11] = pTarget12_[10] *-2; +// pTarget12_[8] = pTarget12_[7] * -2; +// pTarget12_[1] = -pTarget12_[10]; +// pTarget12_[2] = -pTarget12_[11]; +// pTarget12_[4] = -pTarget12_[7]; +// pTarget12_[5] = -pTarget12_[8]; +// pTarget12_[9] = -0.03; +// pTarget12_[0] = -pTarget12_[9]; +// pTarget12_[6] = 0.03; +// pTarget12_[3] = -pTarget12_[6]; + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + + + + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } +// rrr *= float(COUNT )/ schedule_T); +// for(int i=0; i<=1; i++) +// { +// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; +// } +// rrr += abs(gc_[5] - gc_init_[5]) * 2 * stable_reward_rate; +// if(COUNT != 0) +// { +// rrr += (gc_ - gc_init_).norm() * reference_rate *0.1; +// } + + +// rrr +=(gc_init_.head(7) - gc_.head(7)).norm() * stable_reward_rate; + rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; +// rrr += abs(bodyLinearVel_[1]); +// rrr += abs(bodyLinearVel_[2]); +// rrr += abs(bodyLinearVel_[0]); + +// rrr += abs(gc_[2] - gc_init_[2]) ; + +// rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::mi); + rewards_.record("Stable", 1 - rrr, false); + rewards_.record("forwardVel", bodyLinearVel_[0], false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// std::cout<< rot.e().row(2).transpose().size() << " " < ob) final { + /// convert it to float + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + +// / if the contact body is not feet + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// std::cout<<"terminate "<0.3 ) +// { +// return true; +// } +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) +// return true; + +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8 || ) +// return true; + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. 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z{qL4HmkmP?bJUv;kB0omS^jXfOT*B^994Ph>g|6=HK<*SeSg2AI<^f%4|CMIZxg5f9o^g}^vAWqV3?!+cgtM-0{&c^e{(_ z)-aC!JG%MFF!V4-{qL6Bb`C=ib5z%ldj|ed3#N+lHfuIqEJ0X2RbMcYOZm*u#!j$QNa`|6G>-z1MtQe}ab{uAxiM7XRmP z{TpnnCGv-p4?9}_=W6NS;N~fRf`=Wf|8t@9?<<#P5~j}rf%AKc+S int: + return self.ob_dims + + def act_dim(self) -> int: + return self.act_dims + + def alive(self) -> bool: + return True + + def observe(self) -> np.ndarray: + return np.random.rand(1, 34).astype(np.float32) + pass + + def take_action(self, act): + """ + upd recv and send + """ + pass + + def reset(self): + """ + how to implement + maybe to be implement with stand + """ + self.robot.reset() + + + diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml index 5f0f14b7b..31b3a9919 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml @@ -21,7 +21,7 @@ environment: learnning_rate: 5e-4 reward: forwardVel: - coeff: 0.3 + coeff: 0.0 torque: coeff: -4e-5 Stable: diff --git a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py index 123cd1fca..9eca4634f 100755 --- a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py +++ b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py @@ -28,7 +28,7 @@ def tensorboard_launcher(directory_path): tb.configure(argv=[None, '--logdir', directory_path]) url = tb.launch() print("[RAISIM_GYM] Tensorboard session created: "+url) - webbrowser.open_new(url) + # webbrowser.open_new(url) def load_param(weight_path, env, actor, critic, optimizer, data_dir): From 82615d8203e9da039077d29ce638be9ab940afa0 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 1 Aug 2023 13:56:26 +0800 Subject: [PATCH 09/62] add deploy part --- .../raisimGymTorch/env/deploy/deploy.py | 142 ++++++++++++++++++ .../env/deploy/sine_generator.py | 43 ++++++ 2 files changed, 185 insertions(+) create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/deploy.py create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py new file mode 100644 index 000000000..caa5c4cce --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py @@ -0,0 +1,142 @@ +import torch +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO +import argparse +import numpy as np +import time +import os +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.bin.rsg_go1 import NormalSampler +from robot import Robot +import torch.nn as nn +from sine_generator import sine_generator + +robot = Robot() + + + + +parser = argparse.ArgumentParser() +# parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=120) +parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +# mode = args.mode +weight_path = args.weight +weight_path = './data/full_152.pt' +if weight_path!='' and args.cfg_path == None: + cfg_path = os.path.split(weight_path)[0] +'/cfg.yaml' +else: + cfg_path = args.cfg_path +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + +# config +if cfg_path is not None: + cfg = YAML().load(open(cfg_path, 'r')) +else: + cfg = YAML().load(open( + "/cfg.yaml", 'r')) + + +def update_dim(robot): + """ + update the dimentions with robot + make the mid-plugin to get the info using python + + params: + ob_dim: contain the basic info ,which is same with the ob_dim when training + act_dim: contains the action_dim, the number of motors to control + + """ + global act_dim, ob_dim + act_dim = robot.act_dim() + ob_dim = robot.ob_dim() + + +act_dim = 0 +ob_dim = 0 +update_dim(robot) +print(cfg['architecture']['policy_net'], ob_dim, act_dim) +actor = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + + + +def reset(robot): + """ + reset the A1 robot with reset the basic info + robot: to be implemented by c++ + todo + """ + + pass + +def load_model(weight_path, actor): + a = input('Are you sure to load this model and run? Y or N') + if a.lower() == 'y': + checkpoint = torch.load(weight_path) + actor.architecture.load_state_dict(checkpoint['actor_architecture_state_dict']) + # actor.distribution.load_state_dict(checkpoint['actor_distribution_state_dict']) + # critic.architecture.load_state_dict(checkpoint['critic_architecture_state_dict']) + # optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + + +def init(): + weight_path = './data/full_152.pt' + reset(robot) + load_model(weight_path, actor) + + +def cal_limit(act :np.array): + """ + get the limit and norm the act to the limit + param: + act: actions to be clip into the limits + """ + return act + +def act_concate(act:np.array, sine:np.array) -> np.array: + """ + cal the act to the limit and add it with the sine + + """ + sine = np.array(sine) + act = cal_limit(act) + return act + sine + +def run(): + """ + run the ob-act + """ + cnt = 0 + angle_list = [0 for x in range(12)] + while True: + print('running func') + if not robot.alive(): + break + + ob = robot.observe() + + act = actor.architecture(torch.from_numpy(ob).cpu()).cpu().detach().numpy() + + angle_list = sine_generator(angle_list, cnt, cfg['environment']['schedule'], cfg['environment']['angle_rate']) + + act = act_concate(act, angle_list) + + robot.take_action(act) + + time.sleep(0.1) + + cnt += 1 + +if __name__=='__main__': + init() + + run() + diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py b/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py new file mode 100644 index 000000000..1aefb1419 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py @@ -0,0 +1,43 @@ +from math import sin,pi + +def sine_generator(angle_list, idx, T, rate=1): + base1= 0.82 + base3= 0.0 + base2 = -2 * base1 + ang = abs(sin( float(idx) / T * pi)) * rate + + idx_base = 0 + + if (int(idx/T) % 2 ) == 1: + angle_list[idx_base+1] = ang + base1 + angle_list[idx_base+2] = -2 * ang + base2 + + angle_list[idx_base+4] = base1 + angle_list[idx_base+5] = base2 + + angle_list[idx_base+7] = base1 + angle_list[idx_base+8] = base2 + angle_list[idx_base+10] = ang + base1 + angle_list[idx_base+11] = - 2 * ang + base2 + else: + angle_list[idx_base+1] = base1 + angle_list[idx_base+2] = base2 + + angle_list[idx_base+10] = base1 + angle_list[idx_base+11] = base2 + angle_list[idx_base+4] = ang + base1 + angle_list[idx_base+5] = -2 * ang + base2 + + angle_list[idx_base+7] = ang + base1 + angle_list[idx_base+8] = -2 * ang + base2 + angle_list[idx_base+0] = base3 + angle_list[idx_base+3] = base3 + angle_list[idx_base+6] = base3 + angle_list[idx_base+9] = base3 + return angle_list + + +if __name__=='__main__': + angle_list = [0 for x in range(12)] + sine_generator(angle_list, 2, 40, 0.3) + print(angle_list) \ No newline at end of file From 1de92836cb94288310d02c043aa2410a627e1e58 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 8 Aug 2023 14:31:23 +0800 Subject: [PATCH 10/62] deploy part --- examples/CMakeLists.txt | 1 + examples/src/maps/anymals.cpp | 5 +- examples/src/server/go1.cpp | 60 ++- raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py | 7 +- .../raisimGymTorch/env/RaisimGymVecEnv.py | 4 + .../raisimGymTorch/env/deploy/onnx_deploy.py | 268 ++++++++++ .../env/deploy/sine_generator.py | 2 + .../env/envs/rsg/Environment.hpp | 466 ++++++++++++++++++ .../raisimGymTorch/env/envs/rsg/cfg.yaml | 32 ++ .../raisimGymTorch/env/envs/rsg/runner.py | 221 +++++++++ .../raisimGymTorch/env/envs/rsg/tester.py | 76 +++ .../env/envs/rsg_go1/Environment.hpp | 177 +++++-- .../env/envs/rsg_go1/Environment_ylq.hpp | 427 ++++++++++++++++ .../raisimGymTorch/env/envs/rsg_go1/cfg.yaml | 10 +- .../raisimGymTorch/env/envs/rsg_go1/runner.py | 62 ++- raisimGymTorch/raisimGymTorch/env/test.yaml | 32 ++ 16 files changed, 1776 insertions(+), 74 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment.hpp create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp create mode 100644 raisimGymTorch/raisimGymTorch/env/test.yaml diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 87dde6f72..24d95b4bb 100755 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -78,6 +78,7 @@ create_executable(inverseDynamics src/server/inverseDynamics.cpp) create_executable(dzhanibekovEffect src/server/dzhanibekovEffect.cpp) create_executable(go1 src/server/go1.cpp) create_executable(leg src/server/leg.cpp) +create_executable(quat src/server/quat_m.cpp) # xml reader create_executable(xmlRader src/xml/xmlReader.cpp) diff --git a/examples/src/maps/anymals.cpp b/examples/src/maps/anymals.cpp index d4e48882e..c55e2b3ed 100755 --- a/examples/src/maps/anymals.cpp +++ b/examples/src/maps/anymals.cpp @@ -97,7 +97,7 @@ int main(int argc, char* argv[]) { /// create objects auto ground = world.addGround(0, "gnd"); ground->setAppearance("hidden"); - auto anymalC = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\anymal_c\\urdf\\anymal.urdf"); + auto anymalC = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\go1\\go1.urdf"); /// anymalC joint PD controller Eigen::VectorXd jointNominalConfig(anymalC->getGeneralizedCoordinateDim()), jointVelocityTarget(anymalC->getDOF()); @@ -162,11 +162,10 @@ int main(int argc, char* argv[]) { // std::cout<< i<setBasePos(pose); + anymalC->setBasePos(pose); angle_generator(angle_list, i, 30.f, 0.3); jointNominalConfig.tail(12) = angle_list; diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 82374d562..10b7b210e 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -4,14 +4,22 @@ #include "raisim/RaisimServer.hpp" #include "raisim/World.hpp" #define PI 3.1415926 + + +void swap(Eigen::VectorXd &ob, int a, int b) +{ + double tmp = ob[a]; + ob[a] = ob[b]; + ob[b] = ob[a]; +} void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) { // todo ang2, rate, ang // std::cout<< "size is "<< angle_list.size() << std::endl; - double base1= 0.8, base3=0.0; + double base1= 0, base3=0.0; double base2 = -2 * base1; - double ang = -abs(sin( float(idx) / T * PI)) * rate; - double ang2 = -0.15 * ang; + double ang = abs(sin( float(idx) / T * PI)) * rate; + double ang2 = -0.15 * ang * 0; double ang3 = ang2 * 1.2; int idx_base = 0; // std::cout<getGeneralizedCoordinateDim()), jointVelocityTarget(go1->getDOF()); - jointNominalConfig << 0, 0, 0.30, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// jointNominalConfig << 0, 0, 0.41, 1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; + jointNominalConfig << 0, 0, 0.36, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list // jointNominalConfig.tail(12).setZero(); - Eigen::VectorXd tmp(12); - tmp.setZero(); - angle_generator(tmp, 0, 80.f); - jointNominalConfig.tail(12) = tmp; +// Eigen::VectorXd tmp(12); +// tmp.setZero(); +// angle_generator(tmp, 0, 80.f); +// jointNominalConfig.tail(12) = tmp; jointVelocityTarget.setZero(); Eigen::VectorXd jointPgain(go1->getDOF()), jointDgain(go1->getDOF()); - jointPgain.tail(12).setConstant(120.0); - jointDgain.tail(12).setConstant(3); + jointPgain.tail(12).setConstant(2000.0); + jointDgain.tail(12).setConstant(100); go1->setGeneralizedCoordinate(jointNominalConfig); go1->setGeneralizedForce(Eigen::VectorXd::Zero(go1->getDOF())); @@ -89,21 +98,36 @@ int main(int argc, char* argv[]) { server.focusOn(go1); server.launchServer(); Eigen::Vector3d pos(0,0, 3); - + Eigen::VectorXd ob_(24); + ob_.setZero(); for (int i=0; i<2000000; i++) { + go1->getState(jointNominalConfig,jointVelocityTarget); + Eigen::VectorXd tmp1(12), tmp2(12); + tmp1 = jointNominalConfig.tail(12); + tmp2 = jointVelocityTarget.tail(12); + for(auto i=0; i<12; i++){ + ob_[2*i] = tmp1[i]; + ob_[2 * i+1] = tmp2[i]; + } + for(auto i=0;i <12; i++) + { + swap(ob_, i, 12 +i); + } - RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) -// sleep(1); - go1->setBasePos(pos); - angle_generator(tmp, i, 30.f, 0.6); + RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) + std::cout<< ob_ << std::endl<setBasePos(pos); +// angle_generator(tmp, i, 50.f, 0.3); // double rrr = 0; // for(int i=4; i<=6 ; i++) // { // rrr += abs(jointNominalConfig[i] - ) * stable_reward_rate ; // } - jointNominalConfig.tail(12) = tmp; - go1->setPdTarget(jointNominalConfig, jointVelocityTarget); +// jointNominalConfig.tail(12) = tmp; +// std::cout<< tmp << std::endl; +// go1->setPdTarget(jointNominalConfig, jointVelocityTarget); server.integrateWorldThreadSafe(); diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py index 0cad97548..f50835a61 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py @@ -76,10 +76,15 @@ def __init__(self, def act(self, actor_obs): self.actor_obs = actor_obs with torch.no_grad(): - self.actions, self.actions_log_prob = self.actor.sample(torch.from_numpy(actor_obs).to(self.device)) + tmp = torch.from_numpy(actor_obs) + self.actions, self.actions_log_prob = self.actor.sample(tmp.to(self.device)) return self.actions + def forward(self, actor_obs): + return self.act(actor_obs) + + def step(self, value_obs, rews, dones): self.storage.add_transitions(self.actor_obs, value_obs, self.actions, self.actor.action_mean, self.actor.distribution.std_np, rews, dones, self.actions_log_prob) diff --git a/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py b/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py index 882d84873..0aea66510 100755 --- a/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py +++ b/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py @@ -45,6 +45,10 @@ def stop_video_recording(self): self.wrapper.stopRecordingVideo() def step(self, action): + # print(action.size) + # print(type(action)) + # print(action.shape) + # print(action.max(), action.min()) self.wrapper.step(action, self._reward, self._done) return self._reward.copy(), self._done.copy() diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py new file mode 100644 index 000000000..ea541c9b0 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -0,0 +1,268 @@ +import onnxruntime as ort +import onnx +# from mlprodict.onnxrt import OnnxInference +from math import cos,pi +import numpy as np +# 加载模型 + +def deg_rad(x): + if isinstance(x, list): + return [deg_rad(a) for a in x] + else: + return x / 180 * pi + +def rad_deg(x): + if isinstance(x, list): + return [rad_deg(a) for a in x] + else: + return x * pi / 180 +def ang_trans(lower, upper, x): + """ + trans the rate x to the angle between lower and upper + """ + x = (x+1)/2 #todo for test + return x * upper + (1-x) * lower + +def norm(lower, upper, x): + return (x - lower) / (upper - lower) + +def rad_normalize(lower, upper,x): + if isinstance(lower, list): + ans = [] + for a,b,c in zip(lower, upper, x): + ans.append(rad_normalize(a,b,c)) + return ans + else: + ang1 = x * 180 / pi + ang1 = norm(lower, upper, ang1) + return 2*ang1 - 1 + +def deg_normalize(lower, upper, x): + if isinstance(lower, list): + ans = [] + for a,b,c in zip(lower, upper, x): + ans.append(deg_normalize(a,b,c)) + return ans + else: + return 2 * norm(lower, upper, x) - 1 #todo for test + # return 1 - 2* norm(lower, upper,x) + + +low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] +upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] +u0 = [0, 30, -60,0, 30, -60,0, 30, -60,0, 30, -60] +u0 = deg_normalize(low, upp, u0) +# u0 = [-deg_normalize(low, upp, u0)[i] if deg_normalize(low, upp, u0)[i]!=0 else 0 for i in range(12)] +# print(u0) +history_u = u0 +model = ort.InferenceSession('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/deploy/new.onnx') + +idx_local = 0 + +# 准备输入 +input_name = model.get_inputs()[0].name +# print(model.get_outputs()) +output_name = model.get_outputs()[2].name +print(input_name, output_name) +# input_data = np.random.rand(1, 34).astype(np.float32) + +def sine_gene_test_qua(idx, T): + angle_list = [0 for i in range(12)] + if idx >= 2 * T: + idx = 0 + dh = 1 # todo for test + if idx >= 0 and idx <= T: + tp0 =idx - 0 + y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y2 = 0 + elif idx>T and idx<=2*T: + tp0 =idx - T + y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y1 = 0 + + + angle_list[0] = 0 + angle_list[1] = y1 + angle_list[2] = -2 * y1 + angle_list[3] = 0 + angle_list[4] = y2 + angle_list[5] = -2 * y2 + angle_list[6] = 0 + angle_list[7] = y1 + angle_list[8] = -2 * y1 + angle_list[9] = 0 + angle_list[10] = y2 + angle_list[11] = -2 * y2 + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i])) for i in range(12)] + return angle_list + + +def sine_gene(idx, T): + angle_list = [0 for i in range(12)] + if idx >= 2 * T: + idx = 0 + dh = 2 # todo for test + if idx >= 0 and idx <= T: + tp0 =idx - 0 + y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y2 = 0 + elif idx>T and idx<=2*T: + tp0 =idx - T + y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y1 = 0 + + + angle_list[0] = 0 + angle_list[1] = y1 + angle_list[2] = -2 * y1 + angle_list[3] = 0 + angle_list[4] = y2 + angle_list[5] = -2 * y2 + angle_list[6] = 0 + angle_list[7] = y2 + angle_list[8] = -2 * y2 + angle_list[9] = 0 + angle_list[10] = y1 + angle_list[11] = -2 * y1 + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i])) for i in range(12)] + return angle_list + +def rate_to_act(lower, upper, rate): + if isinstance(lower, list): + return [rate_to_act(a,b,c) for a,b,c in zip(lower,upper, rate)] + else: + norm_for_act = lambda a: (a+1)*0.5 + lerp = lambda a, b, c: a*(1-c) + b * c + return lerp(lower, upper, norm_for_act(rate)) + +def add_list(act_gen, sine): + global history_u + kk = 0.9 + kf = 1 + kb = 0.5 + history_u = [history_u[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big + clip = lambda a, b, c: np.clip(a, b, c) + + + # f0 = lambda a: a + # f1 = lambda a, b, c: a + + ans = [rate_to_act(low[i], upp[i], clip(kb * history_u[i] + kf * sine[i], -1, 1)) for i in range(12)] + ans = [deg_rad(x) for x in ans] + # todo swap the ans + # for i in range(6): + # tmp = ans[i+6] + # ans[i+6] = ans[i] + # ans[i] = tmp + return ans + + +def run_model(observation, idx, T): + # todo idx should return 0 when fall + # idx = idx % T + act_gen = model.run([output_name], input_feed={input_name: observation}) + act_gen = act_gen[0] + act_gen = np.zeros((1,12)) + # [array([[]])] + sine = sine_gene(idx, T) + sine = sine_gene_test_qua(idx, T) + # sine = [0 for x in range(12)] + # print(sine,act_gen) + ans = [] + for act in act_gen: + ans.append(add_list(act, sine)) + + ans = [np.array(ans).astype(np.float32)] + + return ans + + +def run_model1(observation): + return model.run([output_name], input_feed={input_name: observation}) + +# 运行模型 + +# outputs = model.run([output_name], input_feed={input_name: input_data}) +# print(outputs) +# x = run_model(input_data) +# print(x) +if __name__=="__main__": + # test_exp = np.zeros((1,34)) + # test_exp1 = np.zeros((1,34)) + # for i in range(12): + # test_exp[0][i * 2 + 10] = -u0[i] + # test_exp1[0][i * 2 + 10] = u0[i] + # # print(test_exp) + # + # ans = run_model(test_exp.astype(np.float32),0, 50) + # ans1 = run_model(test_exp1.astype(np.float32),0, 50) + # # ans1 = run_model(np.zeros((1,34)).astype(np.float32)) + # print(ans) + # print(ans1) + # print(u0) + + a = [ang_trans(low[i], upp[i], u0[i]) for i in range(12)] + print(a) + print(u0) + uu = rate_to_act(low, upp, u0) + uu = deg_rad(uu) + print(uu) + # norm_for_act = lambda a: (a + 1) * 0.5 + # lerp = lambda a, b, c: a * (1 - c) + b * c + # print(deg_normalize(-10,10,6)) + # print(norm_for_act(deg_normalize(-10,10,6))) + # print(lerp(-10, 10,norm_for_act(deg_normalize(-10,10,6)) )) + + # import matplotlib.pyplot as plt + # x = [x for x in range(200)] + # y = [sine_gene(a ,50) for a in x] + # y =np.array(y).transpose() + # + # for i in range(12): + # + # plt.plot(x,y[i]) + # plt.show() + +""" +def test_rad_deg(): + assert rad_deg(1.57)-90 <=1 +def test_rad_deg_list(): + assert isinstance(rad_deg([1, 2,3]), list) + +def test_deg_rad(): + assert deg_rad(90) - 1.57 <= 0.05 + +def test_deg_rad_list(): + assert isinstance(deg_rad([1, 2, 3]), list) + +""" +# def test_sine_gene(): +# for i in range(0, 200, 10): +# a = sine_gene(i, 50) +# print(a) +# assert max(a) <= pi and min(a) >= 0 + +# todo +""" +todo +1. on action 的范围 +2. 似乎是角度问题？应该是范围需要考虑是 角度还是弧度 + + +老师的输入是deg or rad +what's my output + + +using deg to get the rate +and feed the rate to the env + +""" + + +def test_run_model(): + ans = run_model(np.zeros((1, 34)).astype(np.float32), 10, 50) + ans1 = run_model1(np.zeros((1, 34)).astype(np.float32)) + assert ans1[0] == ans[0] + +def test_norm(): + assert deg_normalize(-10, 10, -10) == -1 \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py b/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py index 1aefb1419..bc4ee142d 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py @@ -37,6 +37,8 @@ def sine_generator(angle_list, idx, T, rate=1): return angle_list + + if __name__=='__main__': angle_list = [0 for x in range(12)] sine_generator(angle_list, 2, 40, 0.3) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment.hpp new file mode 100644 index 000000000..82a196374 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment.hpp @@ -0,0 +1,466 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#define PI 3.1415926 + +double cal(double low, double upp, double now){ + return (now + 1)/2 * (upp- low) + low; +} +void swap(Eigen::VectorXd &ob, int a, int b) +{ + double tmp = ob[a]; + ob[a] = ob[b]; + ob[b] = ob[a]; +} + +double rad_deg(double rad) +{ + return rad / PI * 180; +} + +void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); +// std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } +// std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} + +//void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +//{ +//// std::cout<< "size is "<< angle_list.size() << std::endl; +// double base1= 0.82, base3=0.0; +// double base2 = -2 * base1; +// double ang = abs(sin( float(idx) / T * PI)) * rate; +// +// int idx_base = 0; +//// std::cout<(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + /// add objects + anymal_ = world_->addArticulatedSystem(resourceDir_+"/a1_description/urdf/a1.urdf"); + anymal_->setName("dog"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); + nJoints_ = gvDim_ - 6; + + /// initialize containers + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + /// this is nominal configuration of anymal +// gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// anymal_1->setGeneralizedCoordinate(gc_init_); + gc_init_<< 0, 0, 0.41, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; + +// jointNominalConfig.tail(12).setZero(); +// Eigen::VectorXd tmp(12); +// tmp.setZero(); +// angle_generator(tmp, 0, 80.f); +// gc_init_.tail(12) = tmp; + + /// set pd gains + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(2000.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(100); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + + /// MUST BE DONE FOR ALL ENVIRONMENTS + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + +// READ_YAML(); + actionStd_.setConstant(action_std); + + join_limit = anymal_->getJointLimits(); + /// Reward coefficients + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + /// indices of links that should not make contact with ground + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + void init() final { } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + /// action scaling +// COUNT ++; +// std::cout<setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = 0; quat[1] = 0; quat[2] = 0; quat[3] = 0; +// raisim::quatToRotMat(quat, rot); +// anymal_->setBaseOrientation(rot); +// raisim::Vec<4> quat(4); +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// anymal_->setBaseOrientation(quat); +// pTarget_.tail(nJoints_) = angle_list; +// anymal_->setPdTarget(pTarget_, vTarget_); + } + // angle_list *= 0.3; +// angle_list.setZero(); +// Eigen::VectorXd idx1(8), idx2(4); +// idx1.setZero();idx2.setZero(); +// idx1<<1, 2, 4, 5, 7,8,10,11; +// idx2<<0, 3, 6, 9; + + pTarget12_ = action.cast(); +// Eigen::VectorXd ttmp =action.cast(); +// map_from_origin_to_limit(join_limit, pTarget12_,ttmp); + // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); +// pTarget12_ += actionMean_; +// pTarget12_ = angle_list; + + +// angle_mulit(pTarget12_, idx1, action_std); +// angle_mulit(pTarget12_, idx2, action_std); +// +// +// angle_list_for_work = angle_list * for_work_rate; + + + + +// pTarget12_ = angle_list_for_work + pTarget12_; + + + +// pTarget12_[11] = pTarget12_[10] *-2; +// pTarget12_[8] = pTarget12_[7] * -2; +// pTarget12_[1] = -pTarget12_[10]; +// pTarget12_[2] = -pTarget12_[11]; +// pTarget12_[4] = -pTarget12_[7]; +// pTarget12_[5] = -pTarget12_[8]; +// pTarget12_[9] = -0.03; +// pTarget12_[0] = -pTarget12_[9]; +// pTarget12_[6] = 0.03; +// pTarget12_[3] = -pTarget12_[6]; + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + + + + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } +// rrr *= float(COUNT )/ schedule_T); +// for(int i=0; i<=1; i++) +// { +// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; +// } +// rrr += abs(gc_[5] - gc_init_[5]) * 2 * stable_reward_rate; +// if(COUNT != 0) +// { +// rrr += (gc_ - gc_init_).norm() * reference_rate *0.1; +// } + + +// rrr +=(gc_init_.head(7) - gc_.head(7)).norm() * stable_reward_rate; + rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; +// rrr += abs(bodyLinearVel_[1]); +// rrr += abs(bodyLinearVel_[2]); +// rrr += abs(bodyLinearVel_[0]); + +// rrr += abs(gc_[2] - gc_init_[2]) ; + +// rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::mi); + rewards_.record("Stable", 1 - rrr, false); + rewards_.record("forwardVel", bodyLinearVel_[0], false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// std::cout<< rot.e().row(2).transpose().size() << " " < ob) final { + /// convert it to float + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + +// / if the contact body is not feet + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// std::cout<<"terminate "<0.3 ) +// { +// return true; +// } +// if(abs(gc_[4]-gc_init_[4]) >0.2 ) +// { +// return true; +// } +// if(abs(gc_[6]-gc_init_[6]) >0.2 ) +// { +// return true; +// } +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) +// return true; + +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8 || ) +// return true; + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. They are placed to show you how to create a random number sampler. + bool float_base; + std::normal_distribution normDist_; + thread_local static std::mt19937 gen_; +}; +thread_local std::mt19937 raisim::ENVIRONMENT::gen_; + +} + +//int main() +//{ +// raisim::ENVIRONMENT a("../rsc/","/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_anymal_modified/cfg.yaml", +// true); +//} \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml new file mode 100644 index 000000000..deb2a0029 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -0,0 +1,32 @@ +seed: 1 +record_video: false + +environment: + render: true +# just testing commenting + num_envs: 100 + eval_every_n: 100 + num_threads: 30 + simulation_dt: 0.0025 + control_dt: 0.01 + max_time: 8.0 + action_std: 0.01 + show_ref: false + angle_rate: 0.3 + stable: 1 + reference: 0 + schedule: 50 + for_work: 1 + float_base: false + learnning_rate: 5e-4 + reward: + forwardVel: + coeff: 3 + torque: + coeff: -4e-5 + Stable: + coeff: 0.9 + +architecture: + policy_net: [128, 128] + value_net: [128, 128] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py new file mode 100755 index 000000000..41704d49f --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -0,0 +1,221 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger +from raisimGymTorch.env.bin.rsg_go1 import NormalSampler +from raisimGymTorch.env.bin.rsg_go1 import RaisimGymEnv +from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO + + +import os +import math +import time +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse + + + +# task specification +task_name = "go1_locomotion" + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=120) +parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +mode = args.mode +weight_path = args.weight +cfg_path = args.cfg_path +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +if cfg_path is not None: + cfg = YAML().load(open(cfg_path, 'r')) +else: + cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# create environment from the configuration file +env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts +num_threads = cfg['environment']['num_threads'] + +# Training +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * env.num_envs + +avg_rewards = [] + +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + env.num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + +saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, + save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) +logger = RaisimLogger(saver.data_dir+"/train.log") + +if mode =='train' or mode == 'retrain': + tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update + +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=4, + device=device, + log_dir=saver.data_dir, + shuffle_batch=False, + learning_rate=cfg['environment']['learnning_rate'] + ) + +reward_analyzer = RewardAnalyzer(env, ppo.writer) +biggest_reward = 0 +biggest_iter = 0 +if mode == 'retrain': + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +# if load_best == True: +# weight_path = "" +print = logger.info + +mode == 'test' + +total_update = args.update +if mode =='train' or mode == 'retrain': + for update in range(total_update): + start = time.time() + env.reset() + reward_sum = 0 + done_sum = 0 + average_dones = 0. + + if update % cfg['environment']['eval_every_n'] == 0: + print("Visualizing and evaluating the current policy") + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + # we create another graph just to demonstrate the save/load method + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + + env.turn_on_visualization() + env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') + + for step in range(n_steps): + with torch.no_grad(): + frame_start = time.time() + obs = env.observe(False) + + # action = ppo.act(torch.from_numpy(obs).cpu()) + action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + # print(action.min()) + reward, dones = env.step(action.cpu().detach().numpy()) + reward_analyzer.add_reward_info(env.get_reward_info()) + frame_end = time.time() + wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) + if wait_time > 0.: + time.sleep(wait_time) + + env.stop_video_recording() + env.turn_off_visualization() + + reward_analyzer.analyze_and_plot(update) + env.reset() + env.save_scaling(saver.data_dir, str(update)) + + # actual training + for step in range(n_steps): + obs = env.observe() + action = ppo.act(obs) + reward, dones = env.step(action) + ppo.step(value_obs=obs, rews=reward, dones=dones) + done_sum = done_sum + np.sum(dones) + reward_sum = reward_sum + np.sum(reward) + # take st step to get value obs + obs = env.observe() + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) + average_ll_performance = reward_sum / total_steps + if average_ll_performance > biggest_reward: + biggest_iter = update + biggest_reward = average_ll_performance + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + env.save_scaling(saver.data_dir, str(update)) + average_dones = done_sum / total_steps + avg_rewards.append(average_ll_performance) + + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) + + # curriculum update. Implement it in Environment.hpp + env.curriculum_callback() + + end = time.time() + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) + print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) + print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) + print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) + * cfg['environment']['control_dt']))) + print('----------------------------------------------------\n') +else: + env.reset() + start = time.time() + onnx_flag = True + if onnx_flag: + cnt_onnx = 0 + from raisimGymTorch.env.deploy import onnx_deploy + + # load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + for step in range(n_steps * 10): + time.sleep(0.01) + obs = env.observe() + print(obs.shape) + if onnx_flag: + action = onnx_deploy.run_model(obs, cnt_onnx, 50) + action = np.array(action)[0] + cnt_onnx += 1 + # action = np.array([act for x in range(100)]) + else: + action = ppo.act(obs) + + reward, dones = env.step(action) + +print(f'biggest:{biggest_reward},rate = {biggest_iter}') + + diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py new file mode 100755 index 000000000..bdea58d30 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py @@ -0,0 +1,76 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.bin import rsg_anymal +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +import raisimGymTorch.algo.ppo.module as ppo_module +import os +import math +import time +import torch +import argparse + + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-w', '--weight', help='trained weight path', type=str, default='') +args = parser.parse_args() + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# create environment from the configuration file +cfg['environment']['num_envs'] = 1 + +env = VecEnv(rsg_anymal.RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts + +weight_path = args.weight +iteration_number = weight_path.rsplit('/', 1)[1].split('_', 1)[1].rsplit('.', 1)[0] +weight_dir = weight_path.rsplit('/', 1)[0] + '/' + +if weight_path == "": + print("Can't find trained weight, please provide a trained weight with --weight switch\n") +else: + print("Loaded weight from {}\n".format(weight_path)) + start = time.time() + env.reset() + reward_ll_sum = 0 + done_sum = 0 + average_dones = 0. + n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) + total_steps = n_steps * 1 + start_step_id = 0 + + print("Visualizing and evaluating the policy: ", weight_path) + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], torch.nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(weight_path)['actor_architecture_state_dict']) + + env.load_scaling(weight_dir, int(iteration_number)) + env.turn_on_visualization() + + # max_steps = 1000000 + max_steps = 1000 ## 10 secs + + for step in range(max_steps): + time.sleep(0.01) + obs = env.observe(False) + action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + reward_ll, dones = env.step(action_ll.cpu().detach().numpy()) + reward_ll_sum = reward_ll_sum + reward_ll[0] + if dones or step == max_steps - 1: + print('----------------------------------------------------') + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(reward_ll_sum / (step + 1 - start_step_id)))) + print('{:<40} {:>6}'.format("time elapsed [sec]: ", '{:6.4f}'.format((step + 1 - start_step_id) * 0.01))) + print('----------------------------------------------------\n') + start_step_id = step + 1 + reward_ll_sum = 0.0 + + env.turn_off_visualization() + env.reset() + print("Finished at the maximum visualization steps") diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp index 81a9d7751..1667903c1 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment.hpp @@ -14,6 +14,17 @@ double cal(double low, double upp, double now){ return (now + 1)/2 * (upp- low) + low; } +void swap(Eigen::VectorXd &ob, int a, int b) +{ + double tmp = ob[a]; + ob[a] = ob[b]; + ob[b] = tmp; +} + +double rad_deg(double rad) +{ + return rad / PI * 180; +} void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) { @@ -86,9 +97,9 @@ void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1 // std::cout<< "size is "<< angle_list.size() << std::endl; double base1= 0.8, base3=0.0; double base2 = -2 * base1; - double ang = -abs(sin( float(idx) / T * PI)) * rate; - double ang2 = -0.15 * ang; - double ang3 = ang2 * 1.2; + double ang = abs(sin( float(idx) / T * PI)) * rate; +// double ang2 = -0.15 * ang; +// double ang3 = ang2 * 1.2; int idx_base = 0; // std::cout<addArticulatedSystem(resourceDir_+"/a1/urdf/a1.urdf"); + anymal_ = world_->addArticulatedSystem(resourceDir_+"/a1_description/urdf/a1.urdf"); anymal_->setName("dog"); anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); world_->addGround(); @@ -176,18 +187,24 @@ class ENVIRONMENT : public RaisimGymEnv { // gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; // gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; // anymal_1->setGeneralizedCoordinate(gc_init_); - gc_init_<< 0, 0, 0.30, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_<< 0, 0, 0.41, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; +// gc_init_ << 0, 0, 0.36, 1.0, 0.0, 0, 0, 0, -0.4, 0.8529411764705883, 0.0, -0.4, 0.8529411764705883, 0.0, -0.4, 0.8529411764705883, 0.0, -0.4, 0.8529411764705883; // todo implement in python using list + +// gc_init_ << 0, 0, 0.36, 1.0, 0.0, 0, 0, 0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883; // todo implement in python using list +// gc_init_<< 0, 0, 0.41, 1.0, 0.0, -0.4, 0.8529411764705883, 0.0, -0.4, 0.8529411764705883, 0.0, -0.4, 0.8529411764705883, 0.0, -0.4, 0.8529411764705883; // todo implement in python using list + + gc_init_<< 0, 0, 0.36, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list // jointNominalConfig.tail(12).setZero(); - Eigen::VectorXd tmp(12); - tmp.setZero(); - angle_generator(tmp, 0, 80.f); - gc_init_.tail(12) = tmp; +// Eigen::VectorXd tmp(12); +// tmp.setZero(); +// angle_generator(tmp, 0, 80.f); +// gc_init_.tail(12) = tmp; /// set pd gains Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); - jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(120.0); - jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(3); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(2000.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(100); anymal_->setPdGains(jointPgain, jointDgain); anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); @@ -242,9 +259,9 @@ class ENVIRONMENT : public RaisimGymEnv { float step(const Eigen::Ref& action) final { /// action scaling - COUNT ++; +// COUNT ++; // std::cout<(); - Eigen::VectorXd ttmp =action.cast(); - map_from_origin_to_limit(join_limit, pTarget12_,ttmp); +// Eigen::VectorXd ttmp =action.cast(); +// map_from_origin_to_limit(join_limit, pTarget12_,ttmp); // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); // pTarget12_ += actionMean_; // pTarget12_ = angle_list; - angle_mulit(pTarget12_, idx1, action_std); - angle_mulit(pTarget12_, idx2, action_std); +// angle_mulit(pTarget12_, idx1, action_std); +// angle_mulit(pTarget12_, idx2, action_std); +// +// +// angle_list_for_work = angle_list * for_work_rate; + - angle_list_for_work = angle_list * for_work_rate; +// pTarget12_ = angle_list_for_work + pTarget12_; - pTarget12_ = angle_list_for_work + pTarget12_; // pTarget12_[11] = pTarget12_[10] *-2; // pTarget12_[8] = pTarget12_[7] * -2; // pTarget12_[1] = -pTarget12_[10]; @@ -355,6 +375,7 @@ class ENVIRONMENT : public RaisimGymEnv { void updateObservation() { anymal_->getState(gc_, gv_); +// std::cout< quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; @@ -362,16 +383,79 @@ class ENVIRONMENT : public RaisimGymEnv { bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); // std::cout<< rot.e().row(2).transpose().size() << " " < left_q(vec_quat); +// left_q[3] = sqr2 * (quat[0] - quat[1]); // -w +// left_q[0] = -sqr2 * (quat[0] + quat[1]); // x +// left_q[1] = -sqr2 * (quat[2] - quat[3]); // z +// left_q[2] = sqr2 * (quat[2] + quat[3]); // y +// raisim::Mat<3,3> left_rot; +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// raisim::quatToRotMat(new_quat, left_rot); + Eigen::Matrix3d left_rot(new_quat); + bodyLinearVel_ = -left_rot.transpose() * gv_.segment(0, 3); // minus was added to test todo + bodyAngularVel_ = -left_rot.transpose() * gv_.segment(3, 3); +// bodyAngularVel_[0] = -bodyAngularVel_[0]; +// double tt = bodyAngularVel_[1]; +// bodyAngularVel_[1] = -bodyAngularVel_[2]; +// bodyAngularVel_[2] = -tt; + + +// std::cout<< "left " << left_q << "right" << quat << std::endl; + + Eigen::VectorXd tmp1(12), tmp2(12); + tmp1 = gc_.tail(12); + tmp2 = gv_.tail(12); + for(auto i=0;i<=2;i++) bodyAngularVel_[i] = rad_deg(bodyAngularVel_[i]); // to be deg + +// for(auto i=0;i<=11;i++) tmp1[i] = rad_deg(tmp1[i]); // to be deg +// for(auto i=0;i<=11;i++) tmp2[i] = rad_deg(tmp2[i]); // to be deg + obDouble_ << new_quat.x(), + new_quat.y(), + new_quat.z(), + new_quat.w(), +// rot.e().row(2).transpose(), /// body orientation + bodyAngularVel_, + bodyLinearVel_; /// body linear&angular velocity +// tmp1, /// joint angles +// tmp2; /// joint velocity + + for(auto i=0; i<12; i++){ +// std::cout<<2*i + 10 << " " << tmp1[i] << " " << tmp2[i] < ob) final { /// convert it to float ob = obDouble_.cast(); +// std::cout<<"observe in c++\n" << ob <0.3 ) + return true;} +// }if(abs(gc_[3]-gc_init_[3]) >0.3 ) +// { +// return true; +// } +// if(abs(gc_[4]-gc_init_[4]) >0.2 ) // { // return true; // } +// if(abs(gc_[6]-gc_init_[6]) >0.2 ) +// { +// return true; +// } // if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) // return true; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp new file mode 100644 index 000000000..8d582f19d --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp @@ -0,0 +1,427 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#define PI 3.1415926 + +double cal(double low, double upp, double now){ + return (now + 1)/2 * (upp- low) + low; +} + +void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); +// std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } +// std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} + +//void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +//{ +//// std::cout<< "size is "<< angle_list.size() << std::endl; +// double base1= 0.82, base3=0.0; +// double base2 = -2 * base1; +// double ang = abs(sin( float(idx) / T * PI)) * rate; +// +// int idx_base = 0; +//// std::cout<(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + /// add objects + anymal_ = world_->addArticulatedSystem(resourceDir_+"/a1/urdf/a1.urdf"); + anymal_->setName("dog"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); + nJoints_ = gvDim_ - 6; + + /// initialize containers + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + /// this is nominal configuration of anymal +// gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// anymal_1->setGeneralizedCoordinate(gc_init_); + gc_init_<< 0, 0, 0.30, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; + +// jointNominalConfig.tail(12).setZero(); + Eigen::VectorXd tmp(12); + tmp.setZero(); + angle_generator(tmp, 0, 80.f); + gc_init_.tail(12) = tmp; + + /// set pd gains + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(120.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(3); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + + /// MUST BE DONE FOR ALL ENVIRONMENTS + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + +// READ_YAML(); + actionStd_.setConstant(action_std); + + join_limit = anymal_->getJointLimits(); + /// Reward coefficients + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + /// indices of links that should not make contact with ground + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + void init() final { } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + /// action scaling + COUNT ++; +// std::cout<setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = 0; quat[1] = 0; quat[2] = 0; quat[3] = 0; +// raisim::quatToRotMat(quat, rot); +// anymal_->setBaseOrientation(rot); +// raisim::Vec<4> quat(4); +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// anymal_->setBaseOrientation(quat); +// pTarget_.tail(nJoints_) = angle_list; +// anymal_->setPdTarget(pTarget_, vTarget_); + } + // angle_list *= 0.3; +// angle_list.setZero(); + Eigen::VectorXd idx1(8), idx2(4); + idx1.setZero();idx2.setZero(); + idx1<<1, 2, 4, 5, 7,8,10,11; + idx2<<0, 3, 6, 9; + + pTarget12_ = action.cast(); + Eigen::VectorXd ttmp =action.cast(); + map_from_origin_to_limit(join_limit, pTarget12_,ttmp); + // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); +// pTarget12_ += actionMean_; +// pTarget12_ = angle_list; + + + angle_mulit(pTarget12_, idx1, action_std); + angle_mulit(pTarget12_, idx2, action_std); + + + angle_list_for_work = angle_list * for_work_rate; + + + + + pTarget12_ = angle_list_for_work + pTarget12_; +// pTarget12_[11] = pTarget12_[10] *-2; +// pTarget12_[8] = pTarget12_[7] * -2; +// pTarget12_[1] = -pTarget12_[10]; +// pTarget12_[2] = -pTarget12_[11]; +// pTarget12_[4] = -pTarget12_[7]; +// pTarget12_[5] = -pTarget12_[8]; +// pTarget12_[9] = -0.03; +// pTarget12_[0] = -pTarget12_[9]; +// pTarget12_[6] = 0.03; +// pTarget12_[3] = -pTarget12_[6]; + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + + + + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } +// rrr *= float(COUNT )/ schedule_T); +// for(int i=0; i<=1; i++) +// { +// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; +// } +// rrr += abs(gc_[5] - gc_init_[5]) * 2 * stable_reward_rate; +// if(COUNT != 0) +// { +// rrr += (gc_ - gc_init_).norm() * reference_rate *0.1; +// } + + +// rrr +=(gc_init_.head(7) - gc_.head(7)).norm() * stable_reward_rate; + rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; +// rrr += abs(bodyLinearVel_[1]); +// rrr += abs(bodyLinearVel_[2]); +// rrr += abs(bodyLinearVel_[0]); + +// rrr += abs(gc_[2] - gc_init_[2]) ; + +// rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::mi); + rewards_.record("Stable", 1 - rrr, false); + rewards_.record("forwardVel", bodyLinearVel_[0], false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// std::cout<< rot.e().row(2).transpose().size() << " " < ob) final { + /// convert it to float + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + +// / if the contact body is not feet + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// std::cout<<"terminate "<0.3 ) +// { +// return true; +// } +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) +// return true; + +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8 || ) +// return true; + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. They are placed to show you how to create a random number sampler. + bool float_base; + std::normal_distribution normDist_; + thread_local static std::mt19937 gen_; +}; +thread_local std::mt19937 raisim::ENVIRONMENT::gen_; + +} diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml index 32a0b24c9..15ee76666 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml @@ -4,20 +4,20 @@ record_video: false environment: render: true # just testing commenting - num_envs: 100 + num_envs: 1 eval_every_n: 100 - num_threads: 30 + num_threads: 1 simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.018 + action_std: 0.01 show_ref: false angle_rate: 0.3 stable: 1 reference: 0 - schedule: 60 + schedule: 50 for_work: 1 - float_base: false + float_base: False learnning_rate: 5e-4 reward: forwardVel: diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py index 7cf3010aa..880a89c12 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py @@ -103,6 +103,8 @@ # weight_path = "" print = logger.info +mode == 'test' + total_update = args.update if mode =='train' or mode == 'retrain': for update in range(total_update): @@ -194,11 +196,63 @@ else: env.reset() start = time.time() - load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + onnx_flag = True + debug = True + if onnx_flag: + cnt_onnx = 0 + from raisimGymTorch.env.deploy import onnx_deploy + T = 50 + np.set_printoptions(threshold=np.inf) + draw_line =[] + # load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) for step in range(n_steps * 10): time.sleep(0.01) - obs = env.observe() - action = ppo.act(obs) + obs = env.observe(False) + + if debug: + angle = obs[0][[0,1,2,4,5,6]] + angle[0] *= 100 + angle[1] *= 100 + angle[2] *= 100 + draw_line.append(angle.copy()) + + if cnt_onnx == 100: + import matplotlib.pyplot as plt + # print(obs) + colo = ['r','g','b','y','magenta','cyan'] + # colo = ['r','g','b','y'] + # print(angle) + ttt = np.array(draw_line).transpose() + leen = ttt.shape[1] + ze = [0 for x in range(leen)] + xx = [x for x in range(leen)] + t = 0 + for i in ttt: + plt.plot(xx, i, color=colo[t]) + t += 1 + plt.plot(xx, ze) + print(ttt) + plt.show() + input('wait') + + # input('?') + # print(obs.shape) + if onnx_flag: + if cnt_onnx >= 2 * T: + cnt_onnx =0 + action = onnx_deploy.run_model(obs, cnt_onnx, T) + action = np.array(action)[0] + cnt_onnx += 1 + # action = np.array([act for x in range(100)]) + else: + action = ppo.act(obs) + # if cnt_onnx == 1: + # print(action) reward, dones = env.step(action) + if dones[0]: + cnt_onnx = 0 + + +print(f'biggest:{biggest_reward},rate = {biggest_iter}') + -print(f'biggest:{biggest_reward},rate = {biggest_iter}') \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/test.yaml b/raisimGymTorch/raisimGymTorch/env/test.yaml new file mode 100644 index 000000000..21cf408af --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/test.yaml @@ -0,0 +1,32 @@ +seed: 1 +record_video: false + +environment: + render: true +# just testing commenting + num_envs: 100 + eval_every_n: 40 + num_threads: 30 + simulation_dt: 0.0025 + control_dt: 0.01 + max_time: 8.0 + action_std: 0.03 + show_ref: false + angle_rate: 0.3 + stable: 5 + reference: 0 + schedule: 40 + for_work: 1 + float_base: false + learnning_rate: 5e-4 + reward: + forwardVel: + coeff: 0.0 + torque: + coeff: -4e-5 + Stable: + coeff: 0.9 + +architecture: + policy_net: [128, 128] + value_net: [128, 128] From bbe4daf67e624a5649a2f34e2d69efee40de6cbf Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 8 Aug 2023 20:14:32 +0800 Subject: [PATCH 11/62] put the sine generator to python and change the file structure --- examples/src/server/go1.cpp | 9 ++-- raisimGymTorch/CMakeLists.txt | 2 + .../raisimGymTorch/env/RaisimGymEnv.hpp | 2 + .../env/VectorizedEnvironment.hpp | 7 +++ .../{sine_generator.py => angle_utils.py} | 36 ++++++++++++- .../raisimGymTorch/env/deploy/deploy.py | 2 +- .../raisimGymTorch/env/deploy/onnx_deploy.py | 13 +---- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 7 ++- .../raisimGymTorch/env/envs/rsg/runner.py | 48 ++++++++++++++---- .../env/envs/rsg/sine_generator.py | 50 +++++++++++++++++++ .../env/envs/rsg_go1/Environment_ylq.hpp | 1 - .../raisimGymTorch/env/raisim_gym.cpp | 3 +- .../raisimGymTorch/env/utils/transfer_act.py | 0 13 files changed, 150 insertions(+), 30 deletions(-) rename raisimGymTorch/raisimGymTorch/env/deploy/{sine_generator.py => angle_utils.py} (55%) create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py create mode 100644 raisimGymTorch/raisimGymTorch/env/utils/transfer_act.py diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 10b7b210e..0b3ce6deb 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -100,6 +100,7 @@ int main(int argc, char* argv[]) { Eigen::Vector3d pos(0,0, 3); Eigen::VectorXd ob_(24); ob_.setZero(); + size_t dof = go1->getDOF(); for (int i=0; i<2000000; i++) { go1->getState(jointNominalConfig,jointVelocityTarget); Eigen::VectorXd tmp1(12), tmp2(12); @@ -113,10 +114,12 @@ int main(int argc, char* argv[]) { { swap(ob_, i, 12 +i); } - + raisim::Vec<3> acc_; + go1->getFrameAcceleration("ROOT", acc_); + std::cout<setBasePos(pos); // angle_generator(tmp, i, 50.f, 0.3); @@ -127,7 +130,7 @@ int main(int argc, char* argv[]) { // } // jointNominalConfig.tail(12) = tmp; // std::cout<< tmp << std::endl; -// go1->setPdTarget(jointNominalConfig, jointVelocityTarget); + go1->setPdTarget(jointNominalConfig, jointVelocityTarget); server.integrateWorldThreadSafe(); diff --git a/raisimGymTorch/CMakeLists.txt b/raisimGymTorch/CMakeLists.txt index 8c9901416..ba4faa5d6 100755 --- a/raisimGymTorch/CMakeLists.txt +++ b/raisimGymTorch/CMakeLists.txt @@ -76,6 +76,8 @@ find_package(raisim CONFIG REQUIRED) SUBDIRLIST(SUBDIRS ${CMAKE_CURRENT_SOURCE_DIR}/raisimGymTorch/env/envs) set(RAISIMGYM_ENV_DIR ${CMAKE_CURRENT_SOURCE_DIR}/raisimGymTorch/env/envs) +set(SUBDIRS rsg) # only build for rsg + ######## Env ########## FOREACH(subdir ${SUBDIRS}) pybind11_add_module(${subdir} raisimGymTorch/env/raisim_gym.cpp raisimGymTorch/env/Yaml.cpp) diff --git a/raisimGymTorch/raisimGymTorch/env/RaisimGymEnv.hpp b/raisimGymTorch/raisimGymTorch/env/RaisimGymEnv.hpp index f1c4914cf..343069354 100755 --- a/raisimGymTorch/raisimGymTorch/env/RaisimGymEnv.hpp +++ b/raisimGymTorch/raisimGymTorch/env/RaisimGymEnv.hpp @@ -28,6 +28,7 @@ class RaisimGymEnv { /////// implement these methods ///////// virtual void init() = 0; + virtual void init_position(const Eigen::Ref& posi) = 0; virtual void reset() = 0; virtual void observe(Eigen::Ref ob) = 0; virtual float step(const Eigen::Ref& action) = 0; @@ -48,6 +49,7 @@ class RaisimGymEnv { double getSimulationTimeStep() { return simulation_dt_; } raisim::World* getWorld() { return world_.get(); } void turnOffVisualization() { server_->hibernate(); } +// void init_position() {} void turnOnVisualization() { server_->wakeup(); } void startRecordingVideo(const std::string& videoName ) { server_->startRecordingVideo(videoName); } void stopRecordingVideo() { server_->stopRecordingVideo(); } diff --git a/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp b/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp index 6da980fdd..94d3049d3 100755 --- a/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp +++ b/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp @@ -78,6 +78,13 @@ class VectorizedEnvironment { env->reset(); } + void init_position(Eigen::Ref &posi) + { +#pragma omp parallel for schedule(auto) + for(int i = 0; i < num_envs_; i++) + environments_[i]->init_position(posi.row(i)); + } + void observe(Eigen::Ref &ob, bool updateStatistics) { #pragma omp parallel for schedule(auto) for (int i = 0; i < num_envs_; i++) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py similarity index 55% rename from raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py rename to raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py index bc4ee142d..01b6c7fbf 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/sine_generator.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py @@ -1,5 +1,23 @@ from math import sin,pi +import numpy as np +# from onnx_deploy import rad_deg, deg_rad +def deg_rad(x): + if isinstance(x, list): + return [deg_rad(a) for a in x] + else: + return x / 180 * pi + +def rad_deg(x): + if isinstance(x, list): + return [rad_deg(a) for a in x] + else: + return x * pi / 180 + +low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] +upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] +low = deg_rad(low) +upp = deg_rad(upp) def sine_generator(angle_list, idx, T, rate=1): base1= 0.82 base3= 0.0 @@ -36,10 +54,24 @@ def sine_generator(angle_list, idx, T, rate=1): angle_list[idx_base+9] = base3 return angle_list +def transfer(act_gen, sine, k): + """ + clip, lerp, generate the final action + params: act_gen: [12 * (-1, 1)] np.array + params: sine: [12 * rad_target] np.array + """ - + action = act_gen * k + sine * (1-k) + action = np.clip(action, low, upp) + return action if __name__=='__main__': angle_list = [0 for x in range(12)] + sine_generator(angle_list, 2, 40, 0.3) - print(angle_list) \ No newline at end of file + angle_list = np.array(angle_list) + act = transfer(np.random.random((100,12)), angle_list, 0.3) + print('sine:', angle_list) + print('low:', low) + print('upp:', upp) + print(act.shape, type(act)) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py index caa5c4cce..239ead534 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py @@ -9,7 +9,7 @@ from raisimGymTorch.env.bin.rsg_go1 import NormalSampler from robot import Robot import torch.nn as nn -from sine_generator import sine_generator +from angle_utils import sine_generator robot = Robot() diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index ea541c9b0..c20f0a074 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -1,21 +1,12 @@ import onnxruntime as ort -import onnx +# import onnx # from mlprodict.onnxrt import OnnxInference from math import cos,pi import numpy as np +from angle_utils import deg_rad, rad_deg # 加载模型 -def deg_rad(x): - if isinstance(x, list): - return [deg_rad(a) for a in x] - else: - return x / 180 * pi -def rad_deg(x): - if isinstance(x, list): - return [rad_deg(a) for a in x] - else: - return x * pi / 180 def ang_trans(lower, upper, x): """ trans the rate x to the angle between lower and upper diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index deb2a0029..32d37f3c3 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -10,7 +10,7 @@ environment: simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.01 + action_std: 0.03 show_ref: false angle_rate: 0.3 stable: 1 @@ -19,9 +19,12 @@ environment: for_work: 1 float_base: false learnning_rate: 5e-4 + p_gain: 180 + d_gain: 8 + urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: forwardVel: - coeff: 3 + coeff: 0 torque: coeff: -4e-5 Stable: diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 41704d49f..c86e686f4 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -1,12 +1,12 @@ from ruamel.yaml import YAML, dump, RoundTripDumper from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger -from raisimGymTorch.env.bin.rsg_go1 import NormalSampler -from raisimGymTorch.env.bin.rsg_go1 import RaisimGymEnv +from raisimGymTorch.env.bin.rsg import NormalSampler +from raisimGymTorch.env.bin.rsg import RaisimGymEnv from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO - +from raisimGymTorch.env.deploy.angle_utils import transfer import os import math @@ -16,11 +16,22 @@ import torch import datetime import argparse +from sine_generator import sine_generator # task specification -task_name = "go1_locomotion" +task_name = "rsg_test" + + +""" +todo: + 1. init model + 2. change p-d info + 3. debug and add_on sine_gait + +""" + # configuration parser = argparse.ArgumentParser() @@ -46,6 +57,7 @@ cfg = YAML().load(open(cfg_path, 'r')) else: cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) +print(cfg['environment']) # create environment from the configuration file env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) @@ -78,7 +90,7 @@ if mode =='train' or mode == 'retrain': tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update - +num_envs = cfg['environment']['num_envs'] ppo = PPO.PPO(actor=actor, critic=critic, num_envs=cfg['environment']['num_envs'], @@ -99,14 +111,20 @@ if mode == 'retrain': load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) +check_done = lambda a, b: a + 1 if not b else 0 # if load_best == True: # weight_path = "" print = logger.info -mode == 'test' +mode == 'train' total_update = args.update if mode =='train' or mode == 'retrain': + # print('start train') + schedule = cfg['environment']['schedule'] + angle_rate = cfg['environment']['angle_rate'] + act_rate = cfg['environment']['action_std'] # how many action generated use for work + act_rate = float(act_rate) for update in range(total_update): start = time.time() env.reset() @@ -128,7 +146,7 @@ env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') - + envs_idx = [0] * num_envs for step in range(n_steps): with torch.no_grad(): frame_start = time.time() @@ -136,8 +154,12 @@ # action = ppo.act(torch.from_numpy(obs).cpu()) action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + action = action.cpu().detach().numpy() + sine = sine_generator(envs_idx, schedule, angle_rate) + action = transfer(action, sine, act_rate).astype(np.float32) # print(action.min()) - reward, dones = env.step(action.cpu().detach().numpy()) + reward, dones = env.step(action) + envs_idx = list(map(check_done, envs_idx, dones)) reward_analyzer.add_reward_info(env.get_reward_info()) frame_end = time.time() wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) @@ -152,10 +174,18 @@ env.save_scaling(saver.data_dir, str(update)) # actual training + # sine = [0] * 12 + envs_idx = [0] * num_envs for step in range(n_steps): obs = env.observe() action = ppo.act(obs) + + + sine = sine_generator(envs_idx, schedule, angle_rate) + action = transfer(action, sine, act_rate).astype(np.float32) + # todo the transfer has bug reward, dones = env.step(action) + envs_idx = list(map(check_done, envs_idx, dones)) ppo.step(value_obs=obs, rews=reward, dones=dones) done_sum = done_sum + np.sum(dones) reward_sum = reward_sum + np.sum(reward) @@ -205,7 +235,7 @@ for step in range(n_steps * 10): time.sleep(0.01) obs = env.observe() - print(obs.shape) + # print(obs.shape) if onnx_flag: action = onnx_deploy.run_model(obs, cnt_onnx, 50) action = np.array(action)[0] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py new file mode 100644 index 000000000..fb0947df9 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py @@ -0,0 +1,50 @@ +from math import sin,pi +import numpy as np +def sine_generator(idx, T, rate=1): + if isinstance(idx, int): + base1= 0.82 + base3= 0.0 + base2 = -2 * base1 + ang = abs(sin( float(idx) / T * pi)) * rate + angle_list = [0] * 12 + idx_base = 0 + + if (int(idx/T) % 2 ) == 1: + angle_list[idx_base+1] = ang + base1 + angle_list[idx_base+2] = -2 * ang + base2 + + angle_list[idx_base+4] = base1 + angle_list[idx_base+5] = base2 + + angle_list[idx_base+7] = base1 + angle_list[idx_base+8] = base2 + angle_list[idx_base+10] = ang + base1 + angle_list[idx_base+11] = - 2 * ang + base2 + else: + angle_list[idx_base+1] = base1 + angle_list[idx_base+2] = base2 + + angle_list[idx_base+10] = base1 + angle_list[idx_base+11] = base2 + angle_list[idx_base+4] = ang + base1 + angle_list[idx_base+5] = -2 * ang + base2 + + angle_list[idx_base+7] = ang + base1 + angle_list[idx_base+8] = -2 * ang + base2 + angle_list[idx_base+0] = base3 + angle_list[idx_base+3] = base3 + angle_list[idx_base+6] = base3 + angle_list[idx_base+9] = base3 + return angle_list + else: + # tmp = [] + tmp= [sine_generator(x, T, rate) for x in idx] + return np.array(tmp) + + +if __name__=='__main__': + # angle_list = [[0 for x in range(12)] for z in range(3)] + idx = [3, 2, 4] + angle_list = sine_generator(idx, 40, 0.3) + print(angle_list) + print(angle_list.shape) \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp index 8d582f19d..5661bbaa0 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/Environment_ylq.hpp @@ -82,7 +82,6 @@ void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd //} void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) { - // todo ang2, rate, ang // std::cout<< "size is "<< angle_list.size() << std::endl; double base1= 0.8, base3=0.0; double base2 = -2 * base1; diff --git a/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp b/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp index a5934edb6..431445229 100755 --- a/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp +++ b/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp @@ -6,7 +6,7 @@ #include #include #include -#include "Environment.hpp" +#include "Environment_tobuildforpython.hpp" #include "VectorizedEnvironment.hpp" namespace py = pybind11; @@ -24,6 +24,7 @@ PYBIND11_MODULE(RAISIMGYM_TORCH_ENV_NAME, m) { .def("reset", &VectorizedEnvironment::reset) .def("observe", &VectorizedEnvironment::observe) .def("step", &VectorizedEnvironment::step) + .def("init_posi", &VectorizedEnvironment::init_position) .def("setSeed", &VectorizedEnvironment::setSeed) .def("getRewardInfo", &VectorizedEnvironment::getRewardInfo) .def("close", &VectorizedEnvironment::close) diff --git a/raisimGymTorch/raisimGymTorch/env/utils/transfer_act.py b/raisimGymTorch/raisimGymTorch/env/utils/transfer_act.py new file mode 100644 index 000000000..e69de29bb From 76ae26de03e499916b18958fc6621ae85b5e7d1b Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 11:39:25 +0800 Subject: [PATCH 12/62] 1. add submodule A1_raspi_py 2. archive now module --- raisimGymTorch/A1_raspi_py | 1 + .../raisimGymTorch/env/RaisimGymVecEnv.py | 3 + .../raisimGymTorch/env/auto_runner.py | 8 +- .../raisimGymTorch/env/deploy/deploy.py | 1 - .../envs/rsg/Environment_tobuildforpython.hpp | 263 ++++++++++++++++++ .../raisimGymTorch/env/envs/rsg/cfg.yaml | 4 +- .../raisimGymTorch/env/envs/rsg/runner.py | 8 +- .../env/envs/rsg/sim_with_real.py | 164 +++++++++++ .../env/envs/rsg/sine_generator.py | 4 +- .../raisimGymTorch/env/envs/rsg/tester.py | 103 +++---- 10 files changed, 494 insertions(+), 65 deletions(-) create mode 160000 raisimGymTorch/A1_raspi_py create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py diff --git a/raisimGymTorch/A1_raspi_py b/raisimGymTorch/A1_raspi_py new file mode 160000 index 000000000..a565b08d1 --- /dev/null +++ b/raisimGymTorch/A1_raspi_py @@ -0,0 +1 @@ +Subproject commit a565b08d1bde6f797782e88d85b3f7361c4a6f8d diff --git a/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py b/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py index 0aea66510..bec722c02 100755 --- a/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py +++ b/raisimGymTorch/raisimGymTorch/env/RaisimGymVecEnv.py @@ -32,6 +32,9 @@ def __init__(self, impl, normalize_ob=True, seed=0, clip_obs=10.): def seed(self, seed=None): self.wrapper.setSeed(seed) + def init_position(self, position): + self.wrapper.init_position(position) + def turn_on_visualization(self): self.wrapper.turnOnVisualization() diff --git a/raisimGymTorch/raisimGymTorch/env/auto_runner.py b/raisimGymTorch/raisimGymTorch/env/auto_runner.py index aff020b07..be137ccd6 100644 --- a/raisimGymTorch/raisimGymTorch/env/auto_runner.py +++ b/raisimGymTorch/raisimGymTorch/env/auto_runner.py @@ -11,16 +11,16 @@ def change_data(path, flag, data): with open(path, 'w') as f : dump(y, f, Dumper=RoundTripDumper) -test_list = [x/1000 for x in range(0,20,2)] -test_T = [x for x in range(30, 70, 10)] +test_list = [x/1000 for x in range(2,20,2)] +test_T = [x for x in range(40, 70, 10)] # print(test_list) length = 300 for j in test_T: for i in test_list: - change_data('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/cfg.yaml',['action_std','schedule'], [i, j]) + change_data('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml',['action_std','schedule'], [i, j]) if i == 0: continue else: - os.system(f'python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py -u {length} |grep biggest' ) + os.system(f'python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py -u {length} |grep biggest' ) # os.system('python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py -u 3 |grep biggest' ) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py index 239ead534..d67c5acac 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/deploy.py @@ -11,7 +11,6 @@ import torch.nn as nn from angle_utils import sine_generator -robot = Robot() diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp new file mode 100644 index 000000000..0e6fe0644 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -0,0 +1,263 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#include +#define PI 3.1415926 +/*todo + * 1. test the acceleration + * 2. change the terminator + * 3. change reward + */ +namespace raisim { + +class ENVIRONMENT : public RaisimGymEnv { + + public: + + explicit ENVIRONMENT(const std::string& resourceDir, const Yaml::Node& cfg, bool visualizable) : + RaisimGymEnv(resourceDir, cfg), visualizable_(visualizable), normDist_(0, 1) { + /// create world + world_ = std::make_unique(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + READ_YAML(std::string, urdf_path, cfg_["urdf_path"]); + READ_YAML(double, p_gain, cfg_["p_gain"]); + READ_YAML(double, d_gain, cfg_["d_gain"]); + anymal_ = world_->addArticulatedSystem(urdf_path); + anymal_->setName("model"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); // 18 + nJoints_ = gvDim_ - 6; + + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + acc_.setZero();//1 + +// init_position(); + gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883; // todo implement in python using list +// init_position(gc_init_); + init(); + + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + + actionStd_.setConstant(action_std); + + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + + + void init() final { + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(p_gain); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(d_gain); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(urdf_path); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + std::cout<<"position inited\n"; + } + + void init_position(const Eigen::Ref& posi) final + { + gc_init_ = posi.cast(); + init(); + } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + if (show_ref) + { + Eigen::Vector3d po(3, 3 ,10); + anymal_1->setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + } + + pTarget12_ = action.cast(); + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } +// rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; + rewards_.record("Stable", 1 - rrr, false); + rewards_.record("forwardVel", bodyLinearVel_[0], false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + +// auto get_orientation() +// { +// // todo return the orientation +// raisim::Vec<4> quat; +// +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// return quat.e(); +// } +// +// auto get_body_linear_velocity(){ +// return bodyLinearVel_; +// } +// auto get_body_angular_velocity() +// { +// return bodyAngularVel_; +// } +// +// auto get_joint_coordinate() +// { +// return gc_; +// } +// +// auto get_joint_velocity() +// { +// return gv_; +// } + void updateObservation() { + anymal_->getState(gc_, gv_); + std::string root_name = "ROOT"; + anymal_->getFrameAcceleration(root_name , acc_); // todo find the acc + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); + Eigen::VectorXd gcc = gc_.tail(12); + Eigen::VectorXd gvv = gv_.tail(12); + Eigen::VectorXd c_v(24); + for(auto i = 0; i <12 ; i++ ) + { + c_v << gcc[i], gvv[i]; + } + obDouble_ << quat, // quaternion + bodyAngularVel_, // ras/s + acc_, // m/s^2 +// gc_.tail(12), // rad/s +// gv_.tail(12); // rad/s^2 + c_v; + + } + + void observe(Eigen::Ref ob) final { + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + + Eigen::Quaternion quat(gc_[3], gc_[4], gc_[5], gc_[6]); + Eigen::Vector3d euler = quat.toRotationMatrix().eulerAngles(0,1,2); + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over + rewards_.record("Stable", -10, false); + return true;} + if(abs(gc_[2] - gc_init_[2]) < 0.5) return false; + if(fmin(abs(euler[1]), abs(euler[1] - 360.0)) > 0.175) return false; + if(fmin(abs(euler[0]), abs(euler[0] - 360.0)) > 0.175) return false; + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Vec<3> acc_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + double p_gain,d_gain; + std::string urdf_path; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. They are placed to show you how to create a random number sampler. + bool float_base; + std::normal_distribution normDist_; + thread_local static std::mt19937 gen_; +}; +thread_local std::mt19937 raisim::ENVIRONMENT::gen_; + +} diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 32d37f3c3..88ff4cea2 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -10,12 +10,12 @@ environment: simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.03 + action_std: 0.010 show_ref: false angle_rate: 0.3 stable: 1 reference: 0 - schedule: 50 + schedule: 40 for_work: 1 float_base: false learnning_rate: 5e-4 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index c86e686f4..0d0e9d46b 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -151,13 +151,11 @@ with torch.no_grad(): frame_start = time.time() obs = env.observe(False) - - # action = ppo.act(torch.from_numpy(obs).cpu()) action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) action = action.cpu().detach().numpy() sine = sine_generator(envs_idx, schedule, angle_rate) action = transfer(action, sine, act_rate).astype(np.float32) - # print(action.min()) + reward, dones = env.step(action) envs_idx = list(map(check_done, envs_idx, dones)) reward_analyzer.add_reward_info(env.get_reward_info()) @@ -226,7 +224,7 @@ else: env.reset() start = time.time() - onnx_flag = True + onnx_flag = False if onnx_flag: cnt_onnx = 0 from raisimGymTorch.env.deploy import onnx_deploy @@ -234,7 +232,7 @@ # load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) for step in range(n_steps * 10): time.sleep(0.01) - obs = env.observe() + obs = env.observe(False) # print(obs.shape) if onnx_flag: action = onnx_deploy.run_model(obs, cnt_onnx, 50) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py new file mode 100644 index 000000000..d63fe5d38 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -0,0 +1,164 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger +from raisimGymTorch.env.bin.rsg import NormalSampler +from raisimGymTorch.env.bin.rsg import RaisimGymEnv +from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO +from raisimGymTorch.env.deploy.angle_utils import transfer + +import os +import math +import time +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse +from sine_generator import sine_generator + +from raisimGymTorch.env.deploy.py.api import robot as rbt + + + + +# task specification +task_name = "sim2real" + + +""" +todo: + 1. make the model go to the same position + 2. pause check + 3. move + 1. imu info + 2. gyroscope + 3. accelerator + 4. position + 5. velocity + 4. log +""" + + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=120) +parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +mode = args.mode +weight_path = args.weight +cfg_path = args.cfg_path +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +if cfg_path is not None: + cfg = YAML().load(open(cfg_path, 'r')) +else: + cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) +print(cfg['environment']) + +# create environment from the configuration file +env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts +num_threads = cfg['environment']['num_threads'] + +# Training +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * env.num_envs + +avg_rewards = [] + +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + env.num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + +saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, + save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) +logger = RaisimLogger(saver.data_dir+"/train.log") + +if mode =='train' or mode == 'retrain': + tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update +num_envs = cfg['environment']['num_envs'] +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=4, + device=device, + log_dir=saver.data_dir, + shuffle_batch=False, + learning_rate=cfg['environment']['learnning_rate'] + ) + +reward_analyzer = RewardAnalyzer(env, ppo.writer) +biggest_reward = 0 +biggest_iter = 0 +if mode == 'retrain': + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +check_done = lambda a, b: a + 1 if not b else 0 + +print = logger.info + +mode == 'train' + +total_update = args.update + +schedule = cfg['environment']['schedule'] +angle_rate = cfg['environment']['angle_rate'] +act_rate = cfg['environment']['action_std'] # how many action generated use for work +act_rate = float(act_rate) + + +env.reset() +start = time.time() +onnx_flag = False +if onnx_flag: + cnt_onnx = 0 + from raisimGymTorch.env.deploy import onnx_deploy +else: + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +envs_idx = [0] * num_envs + +for step in range(n_steps * 10): + time.sleep(0.01) + obs = env.observe(False) + + if onnx_flag: + action = onnx_deploy.run_model(obs, cnt_onnx, 50) + action = np.array(action)[0] + cnt_onnx += 1 + else: + action = ppo.act(obs) + sine = sine_generator(envs_idx, schedule, angle_rate) + action = transfer(action, sine, act_rate).astype(np.float32) + reward, dones = env.step(action) + envs_idx = list(map(check_done, envs_idx, dones)) + +print(f'biggest:{biggest_reward},rate = {biggest_iter}') + + diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py index fb0947df9..fd6d973bf 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py @@ -2,9 +2,9 @@ import numpy as np def sine_generator(idx, T, rate=1): if isinstance(idx, int): - base1= 0.82 + base1= 0.4 base3= 0.0 - base2 = -2 * base1 + base2 = -0.8529411764705883 ang = abs(sin( float(idx) / T * pi)) * rate angle_list = [0] * 12 idx_base = 0 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py index bdea58d30..f54879d50 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/tester.py @@ -1,5 +1,5 @@ from ruamel.yaml import YAML, dump, RoundTripDumper -from raisimGymTorch.env.bin import rsg_anymal +from raisimGymTorch.env.bin import rsg from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv import raisimGymTorch.algo.ppo.module as ppo_module import os @@ -24,53 +24,54 @@ # create environment from the configuration file cfg['environment']['num_envs'] = 1 -env = VecEnv(rsg_anymal.RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment']) - -# shortcuts -ob_dim = env.num_obs -act_dim = env.num_acts - -weight_path = args.weight -iteration_number = weight_path.rsplit('/', 1)[1].split('_', 1)[1].rsplit('.', 1)[0] -weight_dir = weight_path.rsplit('/', 1)[0] + '/' - -if weight_path == "": - print("Can't find trained weight, please provide a trained weight with --weight switch\n") -else: - print("Loaded weight from {}\n".format(weight_path)) - start = time.time() - env.reset() - reward_ll_sum = 0 - done_sum = 0 - average_dones = 0. - n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) - total_steps = n_steps * 1 - start_step_id = 0 - - print("Visualizing and evaluating the policy: ", weight_path) - loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], torch.nn.LeakyReLU, ob_dim, act_dim) - loaded_graph.load_state_dict(torch.load(weight_path)['actor_architecture_state_dict']) - - env.load_scaling(weight_dir, int(iteration_number)) - env.turn_on_visualization() - - # max_steps = 1000000 - max_steps = 1000 ## 10 secs - - for step in range(max_steps): - time.sleep(0.01) - obs = env.observe(False) - action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) - reward_ll, dones = env.step(action_ll.cpu().detach().numpy()) - reward_ll_sum = reward_ll_sum + reward_ll[0] - if dones or step == max_steps - 1: - print('----------------------------------------------------') - print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(reward_ll_sum / (step + 1 - start_step_id)))) - print('{:<40} {:>6}'.format("time elapsed [sec]: ", '{:6.4f}'.format((step + 1 - start_step_id) * 0.01))) - print('----------------------------------------------------\n') - start_step_id = step + 1 - reward_ll_sum = 0.0 - - env.turn_off_visualization() - env.reset() - print("Finished at the maximum visualization steps") +env = rsg.RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment'] +env.init_position([0] * 12) +print(env) +# # shortcuts +# ob_dim = env.num_obs +# act_dim = env.num_acts +# +# weight_path = args.weight +# iteration_number = weight_path.rsplit('/', 1)[1].split('_', 1)[1].rsplit('.', 1)[0] +# weight_dir = weight_path.rsplit('/', 1)[0] + '/' +# +# if weight_path == "": +# print("Can't find trained weight, please provide a trained weight with --weight switch\n") +# else: +# print("Loaded weight from {}\n".format(weight_path)) +# start = time.time() +# env.reset() +# reward_ll_sum = 0 +# done_sum = 0 +# average_dones = 0. +# n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +# total_steps = n_steps * 1 +# start_step_id = 0 +# +# print("Visualizing and evaluating the policy: ", weight_path) +# loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], torch.nn.LeakyReLU, ob_dim, act_dim) +# loaded_graph.load_state_dict(torch.load(weight_path)['actor_architecture_state_dict']) +# +# env.load_scaling(weight_dir, int(iteration_number)) +# env.turn_on_visualization() +# +# # max_steps = 1000000 +# max_steps = 1000 ## 10 secs +# +# for step in range(max_steps): +# time.sleep(0.01) +# obs = env.observe(False) +# action_ll = loaded_graph.architecture(torch.from_numpy(obs).cpu()) +# reward_ll, dones = env.step(action_ll.cpu().detach().numpy()) +# reward_ll_sum = reward_ll_sum + reward_ll[0] +# if dones or step == max_steps - 1: +# print('----------------------------------------------------') +# print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(reward_ll_sum / (step + 1 - start_step_id)))) +# print('{:<40} {:>6}'.format("time elapsed [sec]: ", '{:6.4f}'.format((step + 1 - start_step_id) * 0.01))) +# print('----------------------------------------------------\n') +# start_step_id = step + 1 +# reward_ll_sum = 0.0 +# +# env.turn_off_visualization() +# env.reset() +# print("Finished at the maximum visualization steps") From 6118d7e2f1c16cb201065721e3262270b031db18 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 11:40:09 +0800 Subject: [PATCH 13/62] 1. add submodule A1_raspi_py 2. archive now module --- .gitmodules | 3 +++ .../env/envs/rsg/Environment_tobuildforpython.hpp | 12 ++++-------- 2 files changed, 7 insertions(+), 8 deletions(-) create mode 100644 .gitmodules diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..7575e7b6c --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "raisimGymTorch/A1_raspi_py"] + path = raisimGymTorch/A1_raspi_py + url = https://github.com/Lr-2002/A1_raspi_py.git diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index 0e6fe0644..e6371edf2 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -11,11 +11,7 @@ #include "../../RaisimGymEnv.hpp" #include #define PI 3.1415926 -/*todo - * 1. test the acceleration - * 2. change the terminator - * 3. change reward - */ + namespace raisim { class ENVIRONMENT : public RaisimGymEnv { @@ -57,7 +53,7 @@ class ENVIRONMENT : public RaisimGymEnv { acc_.setZero();//1 // init_position(); - gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883; // todo implement in python using list + gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883; // init_position(gc_init_); init(); @@ -161,7 +157,7 @@ class ENVIRONMENT : public RaisimGymEnv { // auto get_orientation() // { -// // todo return the orientation +// // raisim::Vec<4> quat; // // quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; @@ -188,7 +184,7 @@ class ENVIRONMENT : public RaisimGymEnv { void updateObservation() { anymal_->getState(gc_, gv_); std::string root_name = "ROOT"; - anymal_->getFrameAcceleration(root_name , acc_); // todo find the acc + anymal_->getFrameAcceleration(root_name , acc_); raisim::Vec<4> quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; From 6306fbb1a821b762180e2c3a91585e3265690b6c Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 12:52:28 +0800 Subject: [PATCH 14/62] 1. add submodule A1_raspi_py 2. archive now module --- raisimGymTorch/A1_raspi_py | 1 - 1 file changed, 1 deletion(-) delete mode 160000 raisimGymTorch/A1_raspi_py diff --git a/raisimGymTorch/A1_raspi_py b/raisimGymTorch/A1_raspi_py deleted file mode 160000 index a565b08d1..000000000 --- a/raisimGymTorch/A1_raspi_py +++ /dev/null @@ -1 +0,0 @@ -Subproject commit a565b08d1bde6f797782e88d85b3f7361c4a6f8d From c0f96f27ef32b326fb500acd279d42a238c2b8f2 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 12:52:30 +0800 Subject: [PATCH 15/62] 1. add submodule A1_raspi_py 2. archive now module --- .../raisimGymTorch/env/envs/rsg/sim_with_real.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index d63fe5d38..80075ad76 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -18,7 +18,12 @@ import argparse from sine_generator import sine_generator -from raisimGymTorch.env.deploy.py.api import robot as rbt +"""" +todo + 1. add rbt + 2. modify lib rbt + +""" From 6910f144c593f9a25d6b10c7cc3a3be3a71ef3a7 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 13:24:04 +0800 Subject: [PATCH 16/62] 1. add submodule A1_raspi_py 2. archive now module --- .../raisimGymTorch/env/deploy/onnx_deploy.py | 1 + .../env/envs/rsg/robot_utils.py | 18 ++++++++++ .../env/envs/rsg/sim_with_real.py | 35 ++++++++++++++++--- 3 files changed, 50 insertions(+), 4 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index c20f0a074..eb1b17997 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -7,6 +7,7 @@ # 加载模型 + def ang_trans(lower, upper, x): """ trans the rate x to the angle between lower and upper diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py new file mode 100644 index 000000000..0465a885f --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py @@ -0,0 +1,18 @@ +from unitree_api import robot as rbt +from unitree_deploy.sine_generator import sine_generator +a1 = rbt.Robot() + +# todo test this after sleep +def init_position(position, timing = 100): + a1.go_position(position, timing) + +def init_robot(): + a1.observe() + act = a1.position + a1.init_motor(act) + + +if __name__=='__main__': + init_robot() + + init_position(position=sine_generator(0, 40, 0.14)) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 80075ad76..6dfe07253 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -16,8 +16,8 @@ import torch import datetime import argparse -from sine_generator import sine_generator - +# from sine_generator import sine_generator +from unitree_deploy.sine_generator import sine_generator """" todo 1. add rbt @@ -28,6 +28,7 @@ + # task specification task_name = "sim2real" @@ -128,7 +129,6 @@ print = logger.info -mode == 'train' total_update = args.update @@ -138,6 +138,25 @@ act_rate = float(act_rate) + +# NOTE : a1 init +moving_robot = False +if moving_robot: + from robot_utils import * + + init_robot() + + ori_posi = sine_generator(0, schedule, rate=angle_rate) # initial position + # a1.kp = cfg['environment']['kp'] + + init_position(ori_posi) + + input('Are you sure to go on?') + + + + + env.reset() start = time.time() onnx_flag = False @@ -148,11 +167,14 @@ load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) envs_idx = [0] * num_envs +if moving_robot : + real_idx = 0 for step in range(n_steps * 10): time.sleep(0.01) obs = env.observe(False) - + if moving_robot: + real_obs = a1.observe() if onnx_flag: action = onnx_deploy.run_model(obs, cnt_onnx, 50) action = np.array(action)[0] @@ -161,6 +183,11 @@ action = ppo.act(obs) sine = sine_generator(envs_idx, schedule, angle_rate) action = transfer(action, sine, act_rate).astype(np.float32) + if moving_robot: + real_action = ppo.act(real_obs) + sine = sine_generator(real_idx, schedule, angle_rate) + real_action = transfer(real_action, sine, act_rate).astype(np.float32) + a1.take_action(real_action) reward, dones = env.step(action) envs_idx = list(map(check_done, envs_idx, dones)) From 918228aec4ad80c1dc7d62de8e71099fba71195a Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 16:29:56 +0800 Subject: [PATCH 17/62] deploy part --- .../raisimGymTorch/env/deploy/angle_utils.py | 5 +- .../envs/rsg/Environment_tobuildforpython.hpp | 2 +- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 4 +- .../env/envs/rsg/robot_utils.py | 29 +++++-- .../raisimGymTorch/env/envs/rsg/runner.py | 78 +++++++++---------- .../env/envs/rsg/sim_with_real.py | 43 +++++++--- .../env/envs/rsg/sine_generator.py | 50 ------------ 7 files changed, 100 insertions(+), 111 deletions(-) delete mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py index 01b6c7fbf..bba1b2e6c 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py @@ -54,13 +54,14 @@ def sine_generator(angle_list, idx, T, rate=1): angle_list[idx_base+9] = base3 return angle_list -def transfer(act_gen, sine, k): +def transfer(act_gen, sine, k) ->np.array: """ clip, lerp, generate the final action params: act_gen: [12 * (-1, 1)] np.array params: sine: [12 * rad_target] np.array """ - + if act_gen.shape[0] == 1: + act_gen = act_gen[0] action = act_gen * k + sine * (1-k) action = np.clip(action, low, upp) return action diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index e6371edf2..fc11d37f9 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -53,7 +53,7 @@ class ENVIRONMENT : public RaisimGymEnv { acc_.setZero();//1 // init_position(); - gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883, 0.0, 0.4, -0.8529411764705883; + gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; // init_position(gc_init_); init(); diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 88ff4cea2..0bf506309 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -10,9 +10,9 @@ environment: simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.010 + action_std: 0.01 show_ref: false - angle_rate: 0.3 + angle_rate: 0.15 stable: 1 reference: 0 schedule: 40 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py index 0465a885f..a23ac60fd 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py @@ -1,18 +1,37 @@ from unitree_api import robot as rbt from unitree_deploy.sine_generator import sine_generator +import signal +import sys a1 = rbt.Robot() # todo test this after sleep def init_position(position, timing = 100): a1.go_position(position, timing) - -def init_robot(): a1.observe() + +def quit_robot(robot): + robot.back_safe() + print('Task finished') + sys.exit(0) + +def signal_handler(signal, frame): + print('You pressed Ctrl+C!') + quit_robot(a1) + +signal.signal(signal.SIGINT, signal_handler) +def init_robot(dt): + a1.dt = dt + print(a1.observe()) + act = a1.position + # print(act) a1.init_motor(act) if __name__=='__main__': - init_robot() - - init_position(position=sine_generator(0, 40, 0.14)) + init_robot(0.01) + print(a1.observe()) + # init_position(position=sine_generator(0, 40, 0.14)) + # + # while True: + # a1.hold_on() diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 0d0e9d46b..0ef130d65 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -16,7 +16,7 @@ import torch import datetime import argparse -from sine_generator import sine_generator +from unitree_deploy.sine_generator import sine_generator @@ -132,44 +132,44 @@ done_sum = 0 average_dones = 0. - if update % cfg['environment']['eval_every_n'] == 0: - print("Visualizing and evaluating the current policy") - torch.save({ - 'actor_architecture_state_dict': actor.architecture.state_dict(), - 'actor_distribution_state_dict': actor.distribution.state_dict(), - 'critic_architecture_state_dict': critic.architecture.state_dict(), - 'optimizer_state_dict': ppo.optimizer.state_dict(), - }, saver.data_dir+"/full_"+str(update)+'.pt') - # we create another graph just to demonstrate the save/load method - loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) - loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) - - env.turn_on_visualization() - env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') - envs_idx = [0] * num_envs - for step in range(n_steps): - with torch.no_grad(): - frame_start = time.time() - obs = env.observe(False) - action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) - action = action.cpu().detach().numpy() - sine = sine_generator(envs_idx, schedule, angle_rate) - action = transfer(action, sine, act_rate).astype(np.float32) - - reward, dones = env.step(action) - envs_idx = list(map(check_done, envs_idx, dones)) - reward_analyzer.add_reward_info(env.get_reward_info()) - frame_end = time.time() - wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) - if wait_time > 0.: - time.sleep(wait_time) - - env.stop_video_recording() - env.turn_off_visualization() - - reward_analyzer.analyze_and_plot(update) - env.reset() - env.save_scaling(saver.data_dir, str(update)) + # if update % cfg['environment']['eval_every_n'] == 0: + # print("Visualizing and evaluating the current policy") + # torch.save({ + # 'actor_architecture_state_dict': actor.architecture.state_dict(), + # 'actor_distribution_state_dict': actor.distribution.state_dict(), + # 'critic_architecture_state_dict': critic.architecture.state_dict(), + # 'optimizer_state_dict': ppo.optimizer.state_dict(), + # }, saver.data_dir+"/full_"+str(update)+'.pt') + # # we create another graph just to demonstrate the save/load method + # loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + # loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + # + # env.turn_on_visualization() + # env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') + # envs_idx = [0] * num_envs + # for step in range(n_steps): + # with torch.no_grad(): + # frame_start = time.time() + # obs = env.observe(False) + # action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + # action = action.cpu().detach().numpy() + # sine = sine_generator(envs_idx, schedule, angle_rate) + # action = transfer(action, sine, act_rate).astype(np.float32) + # + # reward, dones = env.step(action) + # envs_idx = list(map(check_done, envs_idx, dones)) + # reward_analyzer.add_reward_info(env.get_reward_info()) + # frame_end = time.time() + # wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) + # if wait_time > 0.: + # time.sleep(wait_time) + # + # env.stop_video_recording() + # env.turn_off_visualization() + # + # reward_analyzer.analyze_and_plot(update) + # env.reset() + # env.save_scaling(saver.data_dir, str(update)) # actual training # sine = [0] * 12 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 6dfe07253..59eb7278b 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -16,12 +16,13 @@ import torch import datetime import argparse +# import pandas as pd # from sine_generator import sine_generator from unitree_deploy.sine_generator import sine_generator """" todo - 1. add rbt - 2. modify lib rbt + 1. left-hand right-hand check + 2. raisim data check """ @@ -77,6 +78,7 @@ env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) env.seed(cfg['seed']) +dt = cfg['environment']['control_dt'] # shortcuts ob_dim = env.num_obs act_dim = env.num_acts @@ -140,18 +142,22 @@ # NOTE : a1 init -moving_robot = False +moving_robot = True if moving_robot: from robot_utils import * - init_robot() + init_robot(dt) - ori_posi = sine_generator(0, schedule, rate=angle_rate) # initial position + ori_posi = sine_generator(0, schedule, rate=angle_rate).tolist() # initial position # a1.kp = cfg['environment']['kp'] - + # ori_posi = a1.position init_position(ori_posi) - - input('Are you sure to go on?') + print(f""" + now_posi: {a1.position}, + angle_rate : {angle_rate} + schedule: {schedule} + """) + # input('Are you sure to go on?') @@ -167,14 +173,24 @@ load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) envs_idx = [0] * num_envs -if moving_robot : +if moving_robot: real_idx = 0 for step in range(n_steps * 10): - time.sleep(0.01) + # time.sleep(0.01) obs = env.observe(False) + + print(f""" + virtual_observation: + {obs} + """) + if moving_robot: real_obs = a1.observe() + print(f""" + real_observation + {real_obs} + """) if onnx_flag: action = onnx_deploy.run_model(obs, cnt_onnx, 50) action = np.array(action)[0] @@ -186,8 +202,11 @@ if moving_robot: real_action = ppo.act(real_obs) sine = sine_generator(real_idx, schedule, angle_rate) - real_action = transfer(real_action, sine, act_rate).astype(np.float32) - a1.take_action(real_action) + real_action = transfer(real_action, sine, act_rate) + # real_action = real_action + a1.take_action(real_action.tolist()) + real_idx += 1 + reward, dones = env.step(action) envs_idx = list(map(check_done, envs_idx, dones)) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py deleted file mode 100644 index fd6d973bf..000000000 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sine_generator.py +++ /dev/null @@ -1,50 +0,0 @@ -from math import sin,pi -import numpy as np -def sine_generator(idx, T, rate=1): - if isinstance(idx, int): - base1= 0.4 - base3= 0.0 - base2 = -0.8529411764705883 - ang = abs(sin( float(idx) / T * pi)) * rate - angle_list = [0] * 12 - idx_base = 0 - - if (int(idx/T) % 2 ) == 1: - angle_list[idx_base+1] = ang + base1 - angle_list[idx_base+2] = -2 * ang + base2 - - angle_list[idx_base+4] = base1 - angle_list[idx_base+5] = base2 - - angle_list[idx_base+7] = base1 - angle_list[idx_base+8] = base2 - angle_list[idx_base+10] = ang + base1 - angle_list[idx_base+11] = - 2 * ang + base2 - else: - angle_list[idx_base+1] = base1 - angle_list[idx_base+2] = base2 - - angle_list[idx_base+10] = base1 - angle_list[idx_base+11] = base2 - angle_list[idx_base+4] = ang + base1 - angle_list[idx_base+5] = -2 * ang + base2 - - angle_list[idx_base+7] = ang + base1 - angle_list[idx_base+8] = -2 * ang + base2 - angle_list[idx_base+0] = base3 - angle_list[idx_base+3] = base3 - angle_list[idx_base+6] = base3 - angle_list[idx_base+9] = base3 - return angle_list - else: - # tmp = [] - tmp= [sine_generator(x, T, rate) for x in idx] - return np.array(tmp) - - -if __name__=='__main__': - # angle_list = [[0 for x in range(12)] for z in range(3)] - idx = [3, 2, 4] - angle_list = sine_generator(idx, 40, 0.3) - print(angle_list) - print(angle_list.shape) \ No newline at end of file From 7f1e7bfd394be0c7190949c9d8dc7892b06bf550 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Wed, 9 Aug 2023 21:58:53 +0800 Subject: [PATCH 18/62] modified sim_with_real.py could test on A1 --- .../envs/rsg/Environment_tobuildforpython.hpp | 25 ++++-- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 2 +- .../env/envs/rsg/robot_utils.py | 7 +- .../raisimGymTorch/env/envs/rsg/runner.py | 90 ++++++++++--------- .../env/envs/rsg/sim_with_real.py | 20 +++-- 5 files changed, 84 insertions(+), 60 deletions(-) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index fc11d37f9..2a94685dd 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -41,7 +41,7 @@ class ENVIRONMENT : public RaisimGymEnv { world_->addGround(); /// get robot data - gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gcDim_ = anymal_->getGeneralizedCoordinateDim(); // 19 gvDim_ = anymal_->getDOF(); // 18 nJoints_ = gvDim_ - 6; @@ -53,7 +53,7 @@ class ENVIRONMENT : public RaisimGymEnv { acc_.setZero();//1 // init_position(); - gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; + gc_init_<< 0, 0, 0.32, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; // init_position(gc_init_); init(); @@ -188,22 +188,31 @@ class ENVIRONMENT : public RaisimGymEnv { raisim::Vec<4> quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + quat /= quat.norm(); +// std::cout << quat << std::endl; raisim::quatToRotMat(quat, rot); bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); Eigen::VectorXd gcc = gc_.tail(12); Eigen::VectorXd gvv = gv_.tail(12); +// std::cout << "gcc_ : \n " << gcc << std::endl << "gvv : \n" << gvv << std::endl; Eigen::VectorXd c_v(24); + c_v.setZero(24); for(auto i = 0; i <12 ; i++ ) { - c_v << gcc[i], gvv[i]; + c_v[i*2] = gcc[i]; + c_v[i*2 + 1] = gvv[i]; } - obDouble_ << quat, // quaternion - bodyAngularVel_, // ras/s - acc_, // m/s^2 -// gc_.tail(12), // rad/s -// gv_.tail(12); // rad/s^2 +// std::cout << "quat" << quat << "\n body angular vel" << bodyAngularVel_ << "\n acc " << acc_ << "\nc_v "<< c_v << std::endl; + obDouble_ << quat[0], quat[1], quat[2], quat[3], // quaternion + bodyAngularVel_[0], // ras/s + bodyAngularVel_[1], // ras/s + bodyAngularVel_[2], // ras/s + acc_[0], // m/s^2 + acc_[1], // m/s^2 + acc_[2], // m/s^2 c_v; +// std::cout<< "obdouble \n" << obDouble_ << std::endl; } diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 0bf506309..60d69f9e1 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -10,7 +10,7 @@ environment: simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.01 + action_std: 0.05 show_ref: false angle_rate: 0.15 stable: 1 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py index a23ac60fd..291157427 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py @@ -31,7 +31,8 @@ def init_robot(dt): if __name__=='__main__': init_robot(0.01) print(a1.observe()) - # init_position(position=sine_generator(0, 40, 0.14)) + init_position(position=sine_generator(0, 40, 0.14).tolist()) # - # while True: - # a1.hold_on() + while True: + # print(a1.observe()) + a1.hold_on() diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 0ef130d65..13c2bfec9 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -37,7 +37,7 @@ parser = argparse.ArgumentParser() parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') -parser.add_argument('-u', '--update', help='update times', type=int, default=120) +parser.add_argument('-u', '--update', help='update times', type=int, default=300) parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) # parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) args = parser.parse_args() @@ -47,7 +47,7 @@ # load_best = args.load_best # check if gpu is available device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - +print(device) # directories task_path = os.path.dirname(os.path.realpath(__file__)) home_path = task_path + "/../../../../.." @@ -132,50 +132,55 @@ done_sum = 0 average_dones = 0. - # if update % cfg['environment']['eval_every_n'] == 0: - # print("Visualizing and evaluating the current policy") - # torch.save({ - # 'actor_architecture_state_dict': actor.architecture.state_dict(), - # 'actor_distribution_state_dict': actor.distribution.state_dict(), - # 'critic_architecture_state_dict': critic.architecture.state_dict(), - # 'optimizer_state_dict': ppo.optimizer.state_dict(), - # }, saver.data_dir+"/full_"+str(update)+'.pt') - # # we create another graph just to demonstrate the save/load method - # loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) - # loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) - # - # env.turn_on_visualization() - # env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') - # envs_idx = [0] * num_envs - # for step in range(n_steps): - # with torch.no_grad(): - # frame_start = time.time() - # obs = env.observe(False) - # action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) - # action = action.cpu().detach().numpy() - # sine = sine_generator(envs_idx, schedule, angle_rate) - # action = transfer(action, sine, act_rate).astype(np.float32) - # - # reward, dones = env.step(action) - # envs_idx = list(map(check_done, envs_idx, dones)) - # reward_analyzer.add_reward_info(env.get_reward_info()) - # frame_end = time.time() - # wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) - # if wait_time > 0.: - # time.sleep(wait_time) - # - # env.stop_video_recording() - # env.turn_off_visualization() - # - # reward_analyzer.analyze_and_plot(update) - # env.reset() - # env.save_scaling(saver.data_dir, str(update)) + if update % cfg['environment']['eval_every_n'] == 0: + print("Visualizing and evaluating the current policy") + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + # we create another graph just to demonstrate the save/load method + loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + + env.turn_on_visualization() + env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') + envs_idx = [0] * num_envs + for step in range(n_steps): + with torch.no_grad(): + frame_start = time.time() + obs = env.observe(False) + action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) + action = action.cpu().detach().numpy() + sine = sine_generator(envs_idx, schedule, angle_rate) + action = transfer(action, sine, act_rate).astype(np.float32) + + reward, dones = env.step(action) + envs_idx = list(map(check_done, envs_idx, dones)) + reward_analyzer.add_reward_info(env.get_reward_info()) + frame_end = time.time() + wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) + if wait_time > 0.: + time.sleep(wait_time) + + env.stop_video_recording() + env.turn_off_visualization() + + reward_analyzer.analyze_and_plot(update) + env.reset() + env.save_scaling(saver.data_dir, str(update)) # actual training # sine = [0] * 12 envs_idx = [0] * num_envs for step in range(n_steps): - obs = env.observe() + obs = env.observe(False) + """ + 1. z轴方向的加速度的处理方法 + 2. 为什么后边的轴没有速度？ + + """ action = ppo.act(obs) @@ -188,7 +193,7 @@ done_sum = done_sum + np.sum(dones) reward_sum = reward_sum + np.sum(reward) # take st step to get value obs - obs = env.observe() + obs = env.observe(False) ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) average_ll_performance = reward_sum / total_steps if average_ll_performance > biggest_reward: @@ -242,6 +247,7 @@ else: action = ppo.act(obs) + reward, dones = env.step(action) print(f'biggest:{biggest_reward},rate = {biggest_iter}') diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 59eb7278b..b8f7f171e 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -142,7 +142,8 @@ # NOTE : a1 init -moving_robot = True +moving_robot = False + if moving_robot: from robot_utils import * @@ -175,15 +176,17 @@ envs_idx = [0] * num_envs if moving_robot: real_idx = 0 + for i in range(schedule): + a1.hold_on() for step in range(n_steps * 10): - # time.sleep(0.01) + time.sleep(0.01) obs = env.observe(False) - print(f""" - virtual_observation: - {obs} - """) + # print(f""" + # virtual_observation: + # {obs} + # """) if moving_robot: real_obs = a1.observe() @@ -191,6 +194,11 @@ real_observation {real_obs} """) + # for i in range(schedule): + # a1.hold_on() + # print(a1.observe()) + # real_obs = a1.observe() + # print(real_obs) if onnx_flag: action = onnx_deploy.run_model(obs, cnt_onnx, 50) action = np.array(action)[0] From 6a8a93a73ed88f4b2204ba559025e7d96771a3eb Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Thu, 10 Aug 2023 16:58:04 +0800 Subject: [PATCH 19/62] modified time-waiter --- raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py | 4 +- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 2 +- .../raisimGymTorch/env/envs/rsg/runner.py | 1 - .../env/envs/rsg/sim_with_real.py | 83 ++++++++++--------- .../helper/raisim_gym_helper.py | 12 +-- 5 files changed, 54 insertions(+), 48 deletions(-) diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py index f50835a61..1a1d872d4 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py @@ -76,8 +76,8 @@ def __init__(self, def act(self, actor_obs): self.actor_obs = actor_obs with torch.no_grad(): - tmp = torch.from_numpy(actor_obs) - self.actions, self.actions_log_prob = self.actor.sample(tmp.to(self.device)) + # tmp = torch.from_numpy(actor_obs) + self.actions, self.actions_log_prob = self.actor.sample(torch.from_numpy(self.actor_obs).to(self.device)) return self.actions diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 60d69f9e1..55d4732ed 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -10,7 +10,7 @@ environment: simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.05 + action_std: 0.00 show_ref: false angle_rate: 0.15 stable: 1 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 13c2bfec9..1fdf184cc 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -178,7 +178,6 @@ obs = env.observe(False) """ 1. z轴方向的加速度的处理方法 - 2. 为什么后边的轴没有速度？ """ action = ppo.act(obs) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index b8f7f171e..1f4b50013 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -61,7 +61,7 @@ cfg_path = args.cfg_path # load_best = args.load_best # check if gpu is available -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +device = torch.device('cpu') # directories task_path = os.path.dirname(os.path.realpath(__file__)) @@ -139,10 +139,23 @@ act_rate = cfg['environment']['action_std'] # how many action generated use for work act_rate = float(act_rate) +input(f"Are you sure to execute the action in schedule: {schedule}, angle_rate: {angle_rate}, act_rate(how many action generated from NN used for work): {act_rate}") + +# init virtual part +env.reset() +start = time.time() +onnx_flag = False +if onnx_flag: + cnt_onnx = 0 + from raisimGymTorch.env.deploy import onnx_deploy +else: + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +envs_idx = [0] * num_envs # NOTE : a1 init -moving_robot = False +moving_robot = True if moving_robot: from robot_utils import * @@ -164,59 +177,53 @@ -env.reset() -start = time.time() -onnx_flag = False -if onnx_flag: - cnt_onnx = 0 - from raisimGymTorch.env.deploy import onnx_deploy -else: - load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) -envs_idx = [0] * num_envs + if moving_robot: real_idx = 0 for i in range(schedule): a1.hold_on() + # print(f"before work {a1.position}") for step in range(n_steps * 10): - time.sleep(0.01) + if not moving_robot: + print('moving?') + time.sleep(0.01) obs = env.observe(False) - # print(f""" - # virtual_observation: - # {obs} - # """) + #virtual bot part + # if onnx_flag: + # action = onnx_deploy.run_model(obs, cnt_onnx, 50) + # action = np.array(action)[0] + # cnt_onnx += 1 + # else: + # gen_action = ppo.act(obs) + # sine = sine_generator(envs_idx, schedule, angle_rate) + # action = transfer(gen_action, sine, act_rate).astype(np.float32) + # print(f"virtual_obs:{obs} \n virtual_gen_action:{gen_action} \n virtual_action:{action}") + # if real_idx == 0: + # a1.hold_on() + + # real bot part if moving_robot: + print('b-observe') real_obs = a1.observe() - print(f""" - real_observation - {real_obs} - """) - # for i in range(schedule): - # a1.hold_on() - # print(a1.observe()) - # real_obs = a1.observe() - # print(real_obs) - if onnx_flag: - action = onnx_deploy.run_model(obs, cnt_onnx, 50) - action = np.array(action)[0] - cnt_onnx += 1 - else: - action = ppo.act(obs) - sine = sine_generator(envs_idx, schedule, angle_rate) - action = transfer(action, sine, act_rate).astype(np.float32) - if moving_robot: - real_action = ppo.act(real_obs) + print('b-ppo') + gen_action = ppo.act(real_obs) + print('b-sin') sine = sine_generator(real_idx, schedule, angle_rate) - real_action = transfer(real_action, sine, act_rate) + print('b-trans') + real_action = transfer(gen_action, sine, act_rate) # real_action = real_action + print(f"real_obs:{real_obs} \n gen_action : {gen_action}\n for work_action:{real_action}") + a1.take_action(real_action.tolist()) real_idx += 1 - reward, dones = env.step(action) - envs_idx = list(map(check_done, envs_idx, dones)) + # update virtual bot + # reward, dones = env.step(action) + # envs_idx = list(map(check_done, envs_idx, dones)) print(f'biggest:{biggest_reward},rate = {biggest_iter}') diff --git a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py index 9eca4634f..a747eae1e 100755 --- a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py +++ b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py @@ -63,17 +63,17 @@ class RaisimLogger(): def __init__(self, path): self.logger = logging.getLogger(__file__) self.logger.setLevel(logging.INFO) - self.fh = logging.FileHandler(path) + # self.fh = logging.FileHandler(path) self.ch = logging.StreamHandler() - self.fh.setLevel(logging.INFO) + # self.fh.setLevel(logging.INFO) self.ch.setLevel(logging.INFO) - self.formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s", - datefmt="%Y-%m-%d %H:%M:%S") + self.formatter = logging.Formatter("%(asctime)s.%(msecs)03d - %(levelname)s - %(message)s", + datefmt='%Y-%m-%d,%H:%M:%S') self.ch.setFormatter(self.formatter) - self.fh.setFormatter(self.formatter) + # self.fh.setFormatter(self.formatter) self.logger.addHandler(self.ch) - self.logger.addHandler(self.fh) + # self.logger.addHandler(self.fh) def info(self, msg): self.logger.info(msg) From e19d53243b1e42f72f47b8db5b507941b74a586e Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Fri, 11 Aug 2023 19:42:17 +0800 Subject: [PATCH 20/62] a1 trot step finished --- examples/src/server/go1.cpp | 151 ++++++++++++++---- .../raisimGymTorch/deploy_log/anay.py | 8 + .../raisimGymTorch/env/auto_runner.py | 6 +- .../raisimGymTorch/env/deploy/angle_utils.py | 44 +++-- .../envs/rsg/Environment_tobuildforpython.hpp | 99 ++++++++---- .../raisimGymTorch/env/envs/rsg/runner.py | 20 ++- .../env/envs/rsg/sim_with_real.py | 122 ++++++++------ 7 files changed, 324 insertions(+), 126 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/deploy_log/anay.py diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 0b3ce6deb..0a143cd4e 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -4,7 +4,27 @@ #include "raisim/RaisimServer.hpp" #include "raisim/World.hpp" #define PI 3.1415926 - +#include +auto ToEulerAngles(Eigen::Quaterniond q) { + Eigen::Vector3d angles; + + // roll (x-axis rotation) + double sinr_cosp = 2 * (q.w() * q.x() + q.y() * q.z()); + double cosr_cosp = 1 - 2 * (q.x ()* q.x() + q.y() * q.y()); + angles[0] = std::atan2(sinr_cosp, cosr_cosp); + + // pitch (y-axis rotation) + double sinp = std::sqrt(1 + 2 * (q.w ()* q.y ()- q.x() * q.z())); + double cosp = std::sqrt(1 - 2 * (q.w ()* q.y() - q.x() * q.z())); + angles[1] = 2 * std::atan2(sinp, cosp) - M_PI / 2; + + // yaw (z-axis rotation) + double siny_cosp = 2 * (q.w() * q.z() + q.x() * q.y()); + double cosy_cosp = 1 - 2 * (q.y() * q.y() + q.z() * q.z()); + angles[2] = std::atan2(siny_cosp, cosy_cosp); + + return angles; +} void swap(Eigen::VectorXd &ob, int a, int b) { @@ -12,15 +32,60 @@ void swap(Eigen::VectorXd &ob, int a, int b) ob[a] = ob[b]; ob[b] = ob[a]; } +//void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +//{ +// // todo ang2, rate, ang +//// std::cout<< "size is "<< angle_list.size() << std::endl; +// double base1= 0.523, base3=0.0; +// double base2 = -2 * base1; +// double ang = abs(sin( float(idx) / T * PI)) * rate; +// double ang2 = -0.15 * ang * 0; +// double ang3 = ang2 * -2; +// int idx_base = 0; +//// std::cout<getGeneralizedCoordinateDim()), jointVelocityTarget(go1->getDOF()); // jointNominalConfig << 0, 0, 0.41, 1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; - jointNominalConfig << 0, 0, 0.36, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list + jointNominalConfig << 0, 0, 0.33, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list // jointNominalConfig.tail(12).setZero(); // Eigen::VectorXd tmp(12); // tmp.setZero(); @@ -100,12 +163,22 @@ int main(int argc, char* argv[]) { Eigen::Vector3d pos(0,0, 3); Eigen::VectorXd ob_(24); ob_.setZero(); + Eigen::VectorXd gc, gv; + Eigen::VectorXd position, velo; + position.setZero(12), velo.setZero(12); + gc.setZero(19), gv.setZero(18); size_t dof = go1->getDOF(); + + raisim::Vec<3> vel; + vel.setZero(); + raisim::Vec<3> acc; + Eigen::Vector3d ma; + ma.setZero(); for (int i=0; i<2000000; i++) { - go1->getState(jointNominalConfig,jointVelocityTarget); + go1->getState(gc,gv); Eigen::VectorXd tmp1(12), tmp2(12); - tmp1 = jointNominalConfig.tail(12); - tmp2 = jointVelocityTarget.tail(12); + tmp1 = gc.tail(12); + tmp2 = gv.tail(12); for(auto i=0; i<12; i++){ ob_[2*i] = tmp1[i]; ob_[2 * i+1] = tmp2[i]; @@ -114,13 +187,34 @@ int main(int argc, char* argv[]) { { swap(ob_, i, 12 +i); } - raisim::Vec<3> acc_; - go1->getFrameAcceleration("ROOT", acc_); - std::cout< vel_temp; + go1->getFrameVelocity("ROOT", vel_temp); +// for(auto i =0;i <2; i++) +// { + acc = (vel_temp - vel) / 0.01; + vel = vel_temp; +// acc[i] = vel_temp +// } +// std::cout << "vel \n" << vel << std::endl << "acc\n " << acc << std::endl; + usleep(10000); + angle_generator(position, i, 40, 0.15); + jointNominalConfig.tail(12) = position; + + Eigen::Quaterniond quat(gc[3], gc[4], gc[5], gc[6]); + Eigen::Vector3d angle = ToEulerAngles(quat); + if (abs(angle[0]) >abs(ma[0]) ) ma[0] = abs(angle[0]); + if (abs(angle[1]) >abs(ma[1]) ) ma[1] = abs(angle[1]); + if (abs(angle[2]) >abs(ma[2]) ) ma[2] = abs(angle[2]); + +// std::cout<setBasePos(pos); // angle_generator(tmp, i, 50.f, 0.3); // double rrr = 0; @@ -130,7 +224,8 @@ int main(int argc, char* argv[]) { // } // jointNominalConfig.tail(12) = tmp; // std::cout<< tmp << std::endl; - go1->setPdTarget(jointNominalConfig, jointVelocityTarget); + +go1->setPdTarget(jointNominalConfig, jointVelocityTarget); server.integrateWorldThreadSafe(); diff --git a/raisimGymTorch/raisimGymTorch/deploy_log/anay.py b/raisimGymTorch/raisimGymTorch/deploy_log/anay.py new file mode 100644 index 000000000..6d6bcbf51 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/deploy_log/anay.py @@ -0,0 +1,8 @@ +import re +with open("./08-11-15-28", 'r') as f: + lines = f.readlines() + for line in lines: + # x = re.search("work_action", line) + # print(x) + if "work_action" in line: + print(line) \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/auto_runner.py b/raisimGymTorch/raisimGymTorch/env/auto_runner.py index be137ccd6..456a2161c 100644 --- a/raisimGymTorch/raisimGymTorch/env/auto_runner.py +++ b/raisimGymTorch/raisimGymTorch/env/auto_runner.py @@ -11,10 +11,10 @@ def change_data(path, flag, data): with open(path, 'w') as f : dump(y, f, Dumper=RoundTripDumper) -test_list = [x/1000 for x in range(2,20,2)] -test_T = [x for x in range(40, 70, 10)] +test_list = [x/10 for x in range(1,20,4)] +test_T = [40] # print(test_list) -length = 300 +length = 500 for j in test_T: for i in test_list: change_data('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml',['action_std','schedule'], [i, j]) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py index bba1b2e6c..6d2609009 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py @@ -18,8 +18,9 @@ def rad_deg(x): upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] low = deg_rad(low) upp = deg_rad(upp) +bund = [upp[i] - low[i] for i in range(12)] def sine_generator(angle_list, idx, T, rate=1): - base1= 0.82 + base1= 0.523 base3= 0.0 base2 = -2 * base1 ang = abs(sin( float(idx) / T * pi)) * rate @@ -54,12 +55,20 @@ def sine_generator(angle_list, idx, T, rate=1): angle_list[idx_base+9] = base3 return angle_list -def transfer(act_gen, sine, k) ->np.array: +def transfer(act_gen, sine, k, history_act = None) ->np.array: """ clip, lerp, generate the final action params: act_gen: [12 * (-1, 1)] np.array params: sine: [12 * rad_target] np.array """ + # act_gen = (act_gen + 1) /2 # 0-1 + act_gen = act_gen * bund # - + + act_gen = np.clip(act_gen, low, upp) + if history_act is not None: + kk = 0.9 + # act_gen = (1-kk) * act_gen + act_gen = act_gen * (1-kk) + history_act * kk + act_gen = np.clip(act_gen, low, upp) if act_gen.shape[0] == 1: act_gen = act_gen[0] action = act_gen * k + sine * (1-k) @@ -67,12 +76,25 @@ def transfer(act_gen, sine, k) ->np.array: return action if __name__=='__main__': - angle_list = [0 for x in range(12)] - - sine_generator(angle_list, 2, 40, 0.3) - angle_list = np.array(angle_list) - act = transfer(np.random.random((100,12)), angle_list, 0.3) - print('sine:', angle_list) - print('low:', low) - print('upp:', upp) - print(act.shape, type(act)) + # angle_list = [0 for x in range(12)] + # + # sine_generator(angle_list, 0, 40, 0.3) + # angle_list = np.array(angle_list) + # act = transfer(np.zeros((100,12)), angle_list, 1) + # print('sine:', angle_list) + # print('low:', low) + # print('upp:', upp) + # print('bound', bund) + # print(act[0]) + his_util = [0, 0.523, -1.046] * 4 + check_done = lambda a, b: a + 1 if not b else 0 + check_history = lambda a, b: a if not b else his_util + check_zero = lambda a:1 if a!=0 else 0 + tmp1 = np.zeros((100,12)) + idx = np.random.rand(100) + idx[1:10] = False + idx = list(map(check_zero, idx)) + print(idx) + tmp = np.array([check_history(tmp1[i], idx[i]) for i in range(100) ]) + assert tmp1.shape == tmp.shape + print(tmp) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index 2a94685dd..b8ace7b94 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -14,7 +14,28 @@ namespace raisim { -class ENVIRONMENT : public RaisimGymEnv { + auto ToEulerAngles(Eigen::Quaterniond q) { + Eigen::Vector3d angles; + + // roll (x-axis rotation) + double sinr_cosp = 2 * (q.w() * q.x() + q.y() * q.z()); + double cosr_cosp = 1 - 2 * (q.x ()* q.x() + q.y() * q.y()); + angles[0] = std::atan2(sinr_cosp, cosr_cosp); + + // pitch (y-axis rotation) + double sinp = std::sqrt(1 + 2 * (q.w ()* q.y ()- q.x() * q.z())); + double cosp = std::sqrt(1 - 2 * (q.w ()* q.y() - q.x() * q.z())); + angles[1] = 2 * std::atan2(sinp, cosp) - M_PI / 2; + + // yaw (z-axis rotation) + double siny_cosp = 2 * (q.w() * q.z() + q.x() * q.y()); + double cosy_cosp = 1 - 2 * (q.y() * q.y() + q.z() * q.z()); + angles[2] = std::atan2(siny_cosp, cosy_cosp); + + return angles; + } + + class ENVIRONMENT : public RaisimGymEnv { public: @@ -47,17 +68,18 @@ class ENVIRONMENT : public RaisimGymEnv { gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + euler_angle.setZero(); pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); acc_.setZero();//1 // init_position(); - gc_init_<< 0, 0, 0.32, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; + gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; // init_position(gc_init_); init(); - obDim_ = 34; + obDim_ = 31; actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); obDouble_.setZero(obDim_); @@ -85,9 +107,20 @@ class ENVIRONMENT : public RaisimGymEnv { void init() final { Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); - jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(p_gain); - jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(d_gain); + jointPgain.setZero(); + jointPgain.tail(nJoints_).setConstant(p_gain); + jointPgain.tail(nJoints_)[2] = 300; + jointPgain.tail(nJoints_)[5] = 300; + jointPgain.tail(nJoints_)[8] = 300; + jointPgain.tail(nJoints_)[11] = 300; + jointDgain.setZero(); + jointDgain.tail(nJoints_).setConstant(d_gain); + jointDgain.tail(nJoints_)[2] = 15; + jointDgain.tail(nJoints_)[5] = 15; + jointDgain.tail(nJoints_)[8] = 15; + jointDgain.tail(nJoints_)[11] = 15; anymal_->setPdGains(jointPgain, jointDgain); +// std::cout<setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); if(show_ref) @@ -98,7 +131,7 @@ class ENVIRONMENT : public RaisimGymEnv { anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); anymal_1->setPdGains(jointPgain,jointDgain); } - std::cout<<"position inited\n"; +// std::cout<<"position inited\n"; } void init_position(const Eigen::Ref& posi) final @@ -142,16 +175,20 @@ class ENVIRONMENT : public RaisimGymEnv { } updateObservation(); double rrr =0; - for(int i=4; i<=6 ; i++) - { - rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; - } -// rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; - rewards_.record("Stable", 1 - rrr, false); - rewards_.record("forwardVel", bodyLinearVel_[0], false); - - gc_old = gc_.tail(12); - ref_old = angle_list; +// for(int i=4; i<=6 ; i++) +// { +// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; +// } +// for(int i =0;i<=2; i++)rrr += abs(bodyAngularVel_[i]); +//std::cout<getState(gc_, gv_); - std::string root_name = "ROOT"; - anymal_->getFrameAcceleration(root_name , acc_); +// std::string root_name = "ROOT"; +// anymal_->getFrameAcceleration(root_name , acc_); raisim::Vec<4> quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; @@ -208,9 +245,9 @@ class ENVIRONMENT : public RaisimGymEnv { bodyAngularVel_[0], // ras/s bodyAngularVel_[1], // ras/s bodyAngularVel_[2], // ras/s - acc_[0], // m/s^2 - acc_[1], // m/s^2 - acc_[2], // m/s^2 +// acc_[0], // m/s^2 +// acc_[1], // m/s^2 +// acc_[2], // m/s^2 # todo recall acc c_v; // std::cout<< "obdouble \n" << obDouble_ << std::endl; @@ -223,16 +260,23 @@ class ENVIRONMENT : public RaisimGymEnv { bool isTerminalState(float& terminalReward) final { terminalReward = float(terminalRewardCoeff_); - Eigen::Quaternion quat(gc_[3], gc_[4], gc_[5], gc_[6]); - Eigen::Vector3d euler = quat.toRotationMatrix().eulerAngles(0,1,2); + Eigen::Quaterniond quat(gc_[3], gc_[4], gc_[5], gc_[6]); +// std::cout<<"quat" << quat.w() << " " <getContacts()) if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) {// if there is any contact body was not in the footIndices the over - rewards_.record("Stable", -10, false); + rewards_.record("Stable", -10, true); return true;} - if(abs(gc_[2] - gc_init_[2]) < 0.5) return false; - if(fmin(abs(euler[1]), abs(euler[1] - 360.0)) > 0.175) return false; - if(fmin(abs(euler[0]), abs(euler[0] - 360.0)) > 0.175) return false; + if(abs(gc_[2] - gc_init_[2]) > 0.3){ return true;} + if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.1){ return true;} + if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.1){ return true;} + if(fmin(abs(euler_angle[2]), abs(euler_angle[2] + 2 * PI)) > 0.1){ return true;} + if(abs(gc_.tail(12)[0]) >0.17) return true; + if(abs(gc_.tail(12)[3]) >0.17) return true; + if(abs(gc_.tail(12)[6]) >0.17) return true; + if(abs(gc_.tail(12)[9]) >0.17) return true; terminalReward = 0.f; return false; } @@ -258,6 +302,7 @@ class ENVIRONMENT : public RaisimGymEnv { double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; Eigen::VectorXd angle_list, angle_list_for_work; Eigen::VectorXd gc_old, ref_old; + Eigen::Vector3d euler_angle; /// these variables are not in use. They are placed to show you how to create a random number sampler. bool float_base; std::normal_distribution normDist_; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 1fdf184cc..888cbf5e2 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -111,13 +111,17 @@ if mode == 'retrain': load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) +his_util=[0, 0.523, -1.046] * 4 check_done = lambda a, b: a + 1 if not b else 0 +check_history = lambda a, b: a if not b else his_util +# need_to_review = lambda a,b: a if not b else np.array([[0, 0.523, -1.046] * 4]) # todo make sure the shape # if load_best == True: # weight_path = "" + print = logger.info mode == 'train' - +history_act = np.array([his_util] * 100) total_update = args.update if mode =='train' or mode == 'retrain': # print('start train') @@ -147,6 +151,7 @@ env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') envs_idx = [0] * num_envs + for step in range(n_steps): with torch.no_grad(): frame_start = time.time() @@ -154,10 +159,11 @@ action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) action = action.cpu().detach().numpy() sine = sine_generator(envs_idx, schedule, angle_rate) - action = transfer(action, sine, act_rate).astype(np.float32) - + action = transfer(action, sine, act_rate, history_act=history_act).astype(np.float32) reward, dones = env.step(action) envs_idx = list(map(check_done, envs_idx, dones)) + history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) + reward_analyzer.add_reward_info(env.get_reward_info()) frame_end = time.time() wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) @@ -184,9 +190,15 @@ sine = sine_generator(envs_idx, schedule, angle_rate) - action = transfer(action, sine, act_rate).astype(np.float32) + action = transfer(action, sine, act_rate, history_act=history_act).astype(np.float32) # todo the transfer has bug reward, dones = env.step(action) + history_act = sine + envs_idx = list(map(check_done, envs_idx, dones)) + history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) + + reward = reward * envs_idx / n_steps * 2 + envs_idx = list(map(check_done, envs_idx, dones)) ppo.step(value_obs=obs, rews=reward, dones=dones) done_sum = done_sum + np.sum(dones) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 1f4b50013..52620ab9e 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -7,7 +7,7 @@ import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO from raisimGymTorch.env.deploy.angle_utils import transfer - +from unitree_utils.Waiter import Waiter import os import math import time @@ -23,6 +23,8 @@ todo 1. left-hand right-hand check 2. raisim data check + + env cannot get linear-acc """ @@ -34,19 +36,6 @@ task_name = "sim2real" -""" -todo: - 1. make the model go to the same position - 2. pause check - 3. move - 1. imu info - 2. gyroscope - 3. accelerator - 4. position - 5. velocity - 4. log -""" - # configuration parser = argparse.ArgumentParser() @@ -139,23 +128,35 @@ act_rate = cfg['environment']['action_std'] # how many action generated use for work act_rate = float(act_rate) +# act_rate = 0 + input(f"Are you sure to execute the action in schedule: {schedule}, angle_rate: {angle_rate}, act_rate(how many action generated from NN used for work): {act_rate}") + +moving_robot = False +virtual = True + +his_util=[0, 0.523, -1.046] * 4 +# check_done = lambda a, b: a + 1 if not b else 0 +check_history = lambda a, b: a if not b else his_util +history_act = np.array([his_util] * 100) # init virtual part -env.reset() -start = time.time() -onnx_flag = False -if onnx_flag: - cnt_onnx = 0 - from raisimGymTorch.env.deploy import onnx_deploy -else: - load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) +if virtual: + waiter= Waiter(0.01) + env.reset() + start = time.time() + onnx_flag = False + if onnx_flag: + cnt_onnx = 0 + from raisimGymTorch.env.deploy import onnx_deploy + else: + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) -envs_idx = [0] * num_envs + envs_idx = [0] * num_envs # NOTE : a1 init -moving_robot = True + if moving_robot: from robot_utils import * @@ -165,65 +166,80 @@ ori_posi = sine_generator(0, schedule, rate=angle_rate).tolist() # initial position # a1.kp = cfg['environment']['kp'] # ori_posi = a1.position - init_position(ori_posi) + init_position(ori_posi,200) print(f""" now_posi: {a1.position}, angle_rate : {angle_rate} schedule: {schedule} """) - # input('Are you sure to go on?') - - - - - - - -if moving_robot: real_idx = 0 for i in range(schedule): a1.hold_on() - # print(f"before work {a1.position}") for step in range(n_steps * 10): if not moving_robot: + if step == 0: + waiter.update_start() print('moving?') - time.sleep(0.01) - obs = env.observe(False) + time.sleep(0.005) + obs = env.observe(False) #virtual bot part - # if onnx_flag: - # action = onnx_deploy.run_model(obs, cnt_onnx, 50) - # action = np.array(action)[0] - # cnt_onnx += 1 - # else: - # gen_action = ppo.act(obs) - # sine = sine_generator(envs_idx, schedule, angle_rate) - # action = transfer(gen_action, sine, act_rate).astype(np.float32) - # print(f"virtual_obs:{obs} \n virtual_gen_action:{gen_action} \n virtual_action:{action}") - # if real_idx == 0: - # a1.hold_on() + if virtual: + if onnx_flag: + action = onnx_deploy.run_model(obs, cnt_onnx, 50) + action = np.array(action)[0] + cnt_onnx += 1 + else: + gen_action = ppo.act(obs) + sine = sine_generator(envs_idx, schedule, angle_rate) + action = transfer(gen_action, sine, act_rate, history_act=history_act).astype(np.float32) + print(f"virtual_obs:{obs} \n virtual_gen_action:{gen_action} \n virtual_action:{action}") + + # obs = np.zeros((1,31)) + # gen_act = ppo.act(obs) + # sine = sine_generator(real_idx, schedule, angle_rate) + # act = transfer(gen_act, sine, act_rate) + + # real bot part if moving_robot: + if step ==0: + real_history_act = np.array(his_util) print('b-observe') real_obs = a1.observe() + # real_obs = np.array([a1.observe()]) + print(f"real_obs {real_obs}") + # input('now cancel it ') + # print() print('b-ppo') gen_action = ppo.act(real_obs) print('b-sin') - sine = sine_generator(real_idx, schedule, angle_rate) + real_sine = sine_generator(real_idx, schedule, angle_rate) print('b-trans') - real_action = transfer(gen_action, sine, act_rate) + real_action = transfer(gen_action, real_sine, act_rate, history_act=real_history_act) # real_action = real_action print(f"real_obs:{real_obs} \n gen_action : {gen_action}\n for work_action:{real_action}") - + # waiter.wait() + if real_action.shape[0] == 1: + real_action = real_action[0] a1.take_action(real_action.tolist()) + real_history_act = real_sine + # envs_idx = list(map(check_done, real_idx, dones)) + # history_act = np.array([check_history(history_act[i], 0) for i in range(num_envs)]) real_idx += 1 # update virtual bot - # reward, dones = env.step(action) - # envs_idx = list(map(check_done, envs_idx, dones)) + if virtual: + waiter.wait() + reward, dones = env.step(action) + print('exec') + envs_idx = list(map(check_done, envs_idx, dones)) + history_act = sine + envs_idx = list(map(check_done, envs_idx, dones)) + history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) print(f'biggest:{biggest_reward},rate = {biggest_iter}') From 37deb410252045d12a052069a81139157eddaeff Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Sun, 13 Aug 2023 18:30:29 +0800 Subject: [PATCH 21/62] the best perfomance as of 23-08-13-18-30 --- examples/src/server/go1.cpp | 12 +- .../raisimGymTorch/env/deploy/angle_utils.py | 3 +- .../envs/rsg/Environment_tobuildforpython.hpp | 109 ++++++------------ .../raisimGymTorch/env/envs/rsg/cfg.yaml | 23 ++-- .../raisimGymTorch/env/envs/rsg/runner.py | 2 +- 5 files changed, 62 insertions(+), 87 deletions(-) diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 0a143cd4e..1107ca504 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -176,6 +176,8 @@ int main(int argc, char* argv[]) { ma.setZero(); for (int i=0; i<2000000; i++) { go1->getState(gc,gv); + raisim::Vec<4> qu{1+double(i)/500 , 0, 0, 0 + double(i)/500}; + go1->setBaseOrientation(qu); Eigen::VectorXd tmp1(12), tmp2(12); tmp1 = gc.tail(12); tmp2 = gv.tail(12); @@ -189,17 +191,17 @@ int main(int argc, char* argv[]) { } raisim::Vec<3> vel_temp; - go1->getFrameVelocity("ROOT", vel_temp); +// go1->getFrameVelocity("ROOT", vel_temp); // for(auto i =0;i <2; i++) // { - acc = (vel_temp - vel) / 0.01; +// acc = (vel_temp - vel) / 0.01; vel = vel_temp; // acc[i] = vel_temp // } // std::cout << "vel \n" << vel << std::endl << "acc\n " << acc << std::endl; usleep(10000); - angle_generator(position, i, 40, 0.15); - jointNominalConfig.tail(12) = position; +// angle_generator(position, i, 40, 0.15); +// jointNominalConfig.tail(12) = position; Eigen::Quaterniond quat(gc[3], gc[4], gc[5], gc[6]); Eigen::Vector3d angle = ToEulerAngles(quat); @@ -225,7 +227,7 @@ int main(int argc, char* argv[]) { // jointNominalConfig.tail(12) = tmp; // std::cout<< tmp << std::endl; -go1->setPdTarget(jointNominalConfig, jointVelocityTarget); +//go1->setPdTarget(jointNominalConfig, jointVelocityTarget); server.integrateWorldThreadSafe(); diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py index 6d2609009..3c8e16fea 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py @@ -65,13 +65,14 @@ def transfer(act_gen, sine, k, history_act = None) ->np.array: act_gen = act_gen * bund # - + act_gen = np.clip(act_gen, low, upp) if history_act is not None: - kk = 0.9 + kk = 0.8 # act_gen = (1-kk) * act_gen act_gen = act_gen * (1-kk) + history_act * kk act_gen = np.clip(act_gen, low, upp) if act_gen.shape[0] == 1: act_gen = act_gen[0] action = act_gen * k + sine * (1-k) + # print(np.abs(act_gen).max(), sine.max()) action = np.clip(action, low, upp) return action diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index b8ace7b94..0139a433e 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -48,9 +48,6 @@ namespace raisim { READ_YAML(bool, show_ref, cfg_["show_ref"]); if (show_ref == 0) show_ref = false; READ_YAML(double, angle_rate, cfg_["angle_rate"]); - READ_YAML(double, stable_reward_rate, cfg_["stable"]); - READ_YAML(double, reference_rate, cfg_["reference"]); - READ_YAML(double, for_work_rate, cfg_["for_work"]); READ_YAML(bool, float_base, cfg_["float_base"]); READ_YAML(float,schedule_T, cfg_["schedule"]); READ_YAML(std::string, urdf_path, cfg_["urdf_path"]); @@ -68,7 +65,7 @@ namespace raisim { gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); - euler_angle.setZero(); + euler_angle.setZero();euler_angle_old.setZero(); pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); @@ -79,7 +76,7 @@ namespace raisim { // init_position(gc_init_); init(); - obDim_ = 31; + obDim_ = 30; actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); obDouble_.setZero(obDim_); @@ -144,6 +141,7 @@ namespace raisim { anymal_->setState(gc_init_, gv_init_); rewards_.setZero(); COUNT=0; + euler_angle_old.setZero(); updateObservation(); } @@ -162,7 +160,7 @@ namespace raisim { anymal_->setBasePos(po); } - + COUNT ++; pTarget12_ = action.cast(); pTarget_.tail(nJoints_) = pTarget12_; @@ -175,64 +173,37 @@ namespace raisim { } updateObservation(); double rrr =0; -// for(int i=4; i<=6 ; i++) -// { -// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; -// } -// for(int i =0;i<=2; i++)rrr += abs(bodyAngularVel_[i]); -//std::cout< quat; -// -// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; -// return quat.e(); -// } -// -// auto get_body_linear_velocity(){ -// return bodyLinearVel_; -// } -// auto get_body_angular_velocity() -// { -// return bodyAngularVel_; -// } -// -// auto get_joint_coordinate() -// { -// return gc_; -// } -// -// auto get_joint_velocity() -// { -// return gv_; -// } void updateObservation() { anymal_->getState(gc_, gv_); -// std::string root_name = "ROOT"; -// anymal_->getFrameAcceleration(root_name , acc_); raisim::Vec<4> quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; quat /= quat.norm(); -// std::cout << quat << std::endl; raisim::quatToRotMat(quat, rot); bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); + Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); + euler_angle = ToEulerAngles(qua); + if(euler_angle[1] > PI) euler_angle[1] -= 2*PI; + if(euler_angle[0] > PI) euler_angle[0] -= 2*PI; + if(euler_angle[1] < -PI) euler_angle[1] += 2 * PI; + if(euler_angle[0] < -PI) euler_angle[0] += 2 * PI; Eigen::VectorXd gcc = gc_.tail(12); Eigen::VectorXd gvv = gv_.tail(12); -// std::cout << "gcc_ : \n " << gcc << std::endl << "gvv : \n" << gvv << std::endl; Eigen::VectorXd c_v(24); c_v.setZero(24); for(auto i = 0; i <12 ; i++ ) @@ -240,16 +211,11 @@ namespace raisim { c_v[i*2] = gcc[i]; c_v[i*2 + 1] = gvv[i]; } -// std::cout << "quat" << quat << "\n body angular vel" << bodyAngularVel_ << "\n acc " << acc_ << "\nc_v "<< c_v << std::endl; - obDouble_ << quat[0], quat[1], quat[2], quat[3], // quaternion + obDouble_ << euler_angle, // quaternion bodyAngularVel_[0], // ras/s bodyAngularVel_[1], // ras/s bodyAngularVel_[2], // ras/s -// acc_[0], // m/s^2 -// acc_[1], // m/s^2 -// acc_[2], // m/s^2 # todo recall acc c_v; -// std::cout<< "obdouble \n" << obDouble_ << std::endl; } @@ -260,23 +226,24 @@ namespace raisim { bool isTerminalState(float& terminalReward) final { terminalReward = float(terminalRewardCoeff_); - Eigen::Quaterniond quat(gc_[3], gc_[4], gc_[5], gc_[6]); -// std::cout<<"quat" << quat.w() << " " <getContacts()) if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) {// if there is any contact body was not in the footIndices the over - rewards_.record("Stable", -10, true); + rewards_.record("Live", -10, true); return true;} if(abs(gc_[2] - gc_init_[2]) > 0.3){ return true;} - if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.1){ return true;} - if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.1){ return true;} - if(fmin(abs(euler_angle[2]), abs(euler_angle[2] + 2 * PI)) > 0.1){ return true;} - if(abs(gc_.tail(12)[0]) >0.17) return true; - if(abs(gc_.tail(12)[3]) >0.17) return true; - if(abs(gc_.tail(12)[6]) >0.17) return true; - if(abs(gc_.tail(12)[9]) >0.17) return true; + + + if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.17) + { +// std::cout<<"y angle done" << std::endl; + return true; + } + if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.17) + { +// std::cout<<"x angle done" << std::endl; + return true; + } terminalReward = 0.f; return false; } @@ -288,7 +255,7 @@ namespace raisim { bool visualizable_ = false; raisim::ArticulatedSystem* anymal_, *anymal_1; Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; - double terminalRewardCoeff_ = -10.; + double terminalRewardCoeff_ = -30.; Eigen::VectorXd actionMean_, actionStd_, obDouble_; Vec<3> acc_; Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; @@ -299,10 +266,10 @@ namespace raisim { int COUNT = 0; float schedule_T; bool show_ref= true; - double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + double action_std, angle_rate; Eigen::VectorXd angle_list, angle_list_for_work; Eigen::VectorXd gc_old, ref_old; - Eigen::Vector3d euler_angle; + Eigen::Vector3d euler_angle, euler_angle_old; /// these variables are not in use. They are placed to show you how to create a random number sampler. bool float_base; std::normal_distribution normDist_; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 55d4732ed..974c26c4d 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -1,20 +1,19 @@ seed: 1 -record_video: false +record_video: true environment: render: true # just testing commenting num_envs: 100 - eval_every_n: 100 + eval_every_n: 20 num_threads: 30 simulation_dt: 0.0025 control_dt: 0.01 max_time: 8.0 - action_std: 0.00 + action_std: 0.2 +# action_std: 0 show_ref: false - angle_rate: 0.15 - stable: 1 - reference: 0 + angle_rate: 0.5 schedule: 40 for_work: 1 float_base: false @@ -28,8 +27,14 @@ environment: torque: coeff: -4e-5 Stable: - coeff: 0.9 + coeff: 0.1 + Live: + coeff: 0.4 + Wheel: + coeff: 1 + Mimic: + coeff: 0 architecture: - policy_net: [128, 128] - value_net: [128, 128] + policy_net: [512, 512] + value_net: [512, 512] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 888cbf5e2..35be8ad9f 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -197,7 +197,7 @@ envs_idx = list(map(check_done, envs_idx, dones)) history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) - reward = reward * envs_idx / n_steps * 2 + # reward = reward * envs_idx / n_steps * 2 envs_idx = list(map(check_done, envs_idx, dones)) ppo.step(value_obs=obs, rews=reward, dones=dones) From c57caaad189bcfea1c0ddc406542fb3b474cbd81 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 15 Aug 2023 14:30:04 +0800 Subject: [PATCH 22/62] the performance is too weird why the envs_idx is so important? --- examples/src/server/go1.cpp | 21 +++++----- .../raisimGymTorch/env/deploy/angle_utils.py | 41 +++++++++++++------ .../envs/rsg/Environment_tobuildforpython.hpp | 41 +++++++++++++------ .../raisimGymTorch/env/envs/rsg/cfg.yaml | 16 ++++---- .../env/envs/rsg/robot_utils.py | 2 +- .../raisimGymTorch/env/envs/rsg/runner.py | 28 +++++++++---- .../env/envs/rsg/sim_with_real.py | 11 +++-- 7 files changed, 104 insertions(+), 56 deletions(-) diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 1107ca504..b6cd51418 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -132,23 +132,24 @@ int main(int argc, char* argv[]) { /// create objects auto ground = world.addGround(); - auto go1 = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\a1_description\\urdf\\a1.urdf"); + auto go1 = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\a1\\urdf\\a1.urdf"); /// go1 joint PD controller Eigen::VectorXd jointNominalConfig(go1->getGeneralizedCoordinateDim()), jointVelocityTarget(go1->getDOF()); // jointNominalConfig << 0, 0, 0.41, 1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; jointNominalConfig << 0, 0, 0.33, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list +// jointNominalConfig << 0, 0, 0.33, 1.0, 0.0, 0, 0, 0, 1, -2.2, 0.0, 1, -2.2, 0.0, 1, -2.2, 0.0, 1, -2.2; // todo implement in python using list // jointNominalConfig.tail(12).setZero(); // Eigen::VectorXd tmp(12); // tmp.setZero(); // angle_generator(tmp, 0, 80.f); // jointNominalConfig.tail(12) = tmp; jointVelocityTarget.setZero(); - + go1->setControlMode(raisim::ControlMode::FORCE_AND_TORQUE); Eigen::VectorXd jointPgain(go1->getDOF()), jointDgain(go1->getDOF()); - jointPgain.tail(12).setConstant(2000.0); - jointDgain.tail(12).setConstant(100); + jointPgain.tail(12).setConstant(80); + jointDgain.tail(12).setConstant(4); go1->setGeneralizedCoordinate(jointNominalConfig); go1->setGeneralizedForce(Eigen::VectorXd::Zero(go1->getDOF())); @@ -176,8 +177,8 @@ int main(int argc, char* argv[]) { ma.setZero(); for (int i=0; i<2000000; i++) { go1->getState(gc,gv); - raisim::Vec<4> qu{1+double(i)/500 , 0, 0, 0 + double(i)/500}; - go1->setBaseOrientation(qu); +// raisim::Vec<4> qu{1+double(i)/500 , 0, 0, 0 + double(i)/500}; +// go1->setBaseOrientation(qu); Eigen::VectorXd tmp1(12), tmp2(12); tmp1 = gc.tail(12); tmp2 = gv.tail(12); @@ -199,9 +200,9 @@ int main(int argc, char* argv[]) { // acc[i] = vel_temp // } // std::cout << "vel \n" << vel << std::endl << "acc\n " << acc << std::endl; - usleep(10000); -// angle_generator(position, i, 40, 0.15); -// jointNominalConfig.tail(12) = position; + usleep(5000); + angle_generator(position, i, 40, 0.35); + jointNominalConfig.tail(12) = position; Eigen::Quaterniond quat(gc[3], gc[4], gc[5], gc[6]); Eigen::Vector3d angle = ToEulerAngles(quat); @@ -227,7 +228,7 @@ int main(int argc, char* argv[]) { // jointNominalConfig.tail(12) = tmp; // std::cout<< tmp << std::endl; -//go1->setPdTarget(jointNominalConfig, jointVelocityTarget); +go1->setPdTarget(jointNominalConfig, jointVelocityTarget); server.integrateWorldThreadSafe(); diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py index 3c8e16fea..aaca1b211 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py @@ -65,7 +65,7 @@ def transfer(act_gen, sine, k, history_act = None) ->np.array: act_gen = act_gen * bund # - + act_gen = np.clip(act_gen, low, upp) if history_act is not None: - kk = 0.8 + kk = 0.6 # act_gen = (1-kk) * act_gen act_gen = act_gen * (1-kk) + history_act * kk act_gen = np.clip(act_gen, low, upp) @@ -76,6 +76,18 @@ def transfer(act_gen, sine, k, history_act = None) ->np.array: action = np.clip(action, low, upp) return action +def get_last_position(obs): + if isinstance(obs, list): + x = [obs[-2 * i] for i in range(1, 13)] + x.reverse() + return x + elif isinstance(obs, np.ndarray): + x = [] + for i in range(1, 13): + x.append(obs[:, -2*i]) + x.reverse() + return np.stack(x, axis=1) + if __name__=='__main__': # angle_list = [0 for x in range(12)] # @@ -87,15 +99,18 @@ def transfer(act_gen, sine, k, history_act = None) ->np.array: # print('upp:', upp) # print('bound', bund) # print(act[0]) - his_util = [0, 0.523, -1.046] * 4 - check_done = lambda a, b: a + 1 if not b else 0 - check_history = lambda a, b: a if not b else his_util - check_zero = lambda a:1 if a!=0 else 0 - tmp1 = np.zeros((100,12)) - idx = np.random.rand(100) - idx[1:10] = False - idx = list(map(check_zero, idx)) - print(idx) - tmp = np.array([check_history(tmp1[i], idx[i]) for i in range(100) ]) - assert tmp1.shape == tmp.shape - print(tmp) + # his_util = [0, 0.523, -1.046] * 4 + # check_done = lambda a, b: a + 1 if not b else 0 + # check_history = lambda a, b: a if not b else his_util + # check_zero = lambda a:1 if a!=0 else 0 + # tmp1 = np.zeros((100,12)) + # idx = np.random.rand(100) + # idx[1:10] = False + # idx = list(map(check_zero, idx)) + # print(idx) + # tmp = np.array([check_history(tmp1[i], idx[i]) for i in range(100) ]) + # assert tmp1.shape == tmp.shape + # print(tmp) + a = np.random.rand(2,30) + print(a) + print(get_last_position(a).shape) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index 0139a433e..e2e6ea184 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -175,13 +175,15 @@ namespace raisim { double rrr =0; // for(int i=0;i<=2;i++) rrr += abs(euler_angle[i]) ; rrr = abs(euler_angle[0]) + abs(euler_angle[1]); + rrr = abs(gc_[0]) + abs(gc_[1]); // rrr += (gc_.tail(12) - pTarget12_).norm() ; - bool accu = true; + bool accu = false; rewards_.record("Stable", - rrr, accu); rewards_.record("Live", 1, accu); rewards_.record("forwardVel", bodyLinearVel_[0], accu); rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); - rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); +// rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); + rewards_.record("Wheel", euler_angle[2] * 0.1 + (bodyAngularVel_[2] * 1), accu); euler_angle_old = euler_angle; // rewards_.record("torque", ) return rewards_.sum(); @@ -198,10 +200,10 @@ namespace raisim { bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); euler_angle = ToEulerAngles(qua); - if(euler_angle[1] > PI) euler_angle[1] -= 2*PI; - if(euler_angle[0] > PI) euler_angle[0] -= 2*PI; - if(euler_angle[1] < -PI) euler_angle[1] += 2 * PI; - if(euler_angle[0] < -PI) euler_angle[0] += 2 * PI; +// if(euler_angle[1] > PI) euler_angle[1] -= 2*PI; +// if(euler_angle[0] > PI) euler_angle[0] -= 2*PI; +// if(euler_angle[1] < -PI) euler_angle[1] += 2 * PI; +// if(euler_angle[0] < -PI) euler_angle[0] += 2 * PI; Eigen::VectorXd gcc = gc_.tail(12); Eigen::VectorXd gvv = gv_.tail(12); Eigen::VectorXd c_v(24); @@ -211,7 +213,8 @@ namespace raisim { c_v[i*2] = gcc[i]; c_v[i*2 + 1] = gvv[i]; } - obDouble_ << euler_angle, // quaternion + obDouble_ << euler_angle[0], + euler_angle[1],// quaternion bodyAngularVel_[0], // ras/s bodyAngularVel_[1], // ras/s bodyAngularVel_[2], // ras/s @@ -229,21 +232,33 @@ namespace raisim { for(auto& contact: anymal_->getContacts()) if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) {// if there is any contact body was not in the footIndices the over - rewards_.record("Live", -10, true); + rewards_.record("Live", -10, false); return true;} if(abs(gc_[2] - gc_init_[2]) > 0.3){ return true;} - if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.17) + if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.2) { // std::cout<<"y angle done" << std::endl; - return true; + return true; } - if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.17) + if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.2) { // std::cout<<"x angle done" << std::endl; - return true; + return true; } + if (abs(gc_[0] - gc_init_[0]) >= 0.4) + { + return true; + } + if (abs(gc_[1] - gc_init_[1]) >= 0.4) + { + return true; + } + if(euler_angle[2] < -0.3) + { + return true; + } terminalReward = 0.f; return false; } @@ -255,7 +270,7 @@ namespace raisim { bool visualizable_ = false; raisim::ArticulatedSystem* anymal_, *anymal_1; Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; - double terminalRewardCoeff_ = -30.; + double terminalRewardCoeff_ = -10.; Eigen::VectorXd actionMean_, actionStd_, obDouble_; Vec<3> acc_; Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 974c26c4d..167540e16 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -5,19 +5,19 @@ environment: render: true # just testing commenting num_envs: 100 - eval_every_n: 20 + eval_every_n: 50 num_threads: 30 simulation_dt: 0.0025 control_dt: 0.01 - max_time: 8.0 - action_std: 0.2 + max_time: 4 + action_std: 0.03 # action_std: 0 show_ref: false - angle_rate: 0.5 + angle_rate: 0.2 schedule: 40 for_work: 1 float_base: false - learnning_rate: 5e-4 + learnning_rate: 1e-3 p_gain: 180 d_gain: 8 urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf @@ -27,11 +27,11 @@ environment: torque: coeff: -4e-5 Stable: - coeff: 0.1 + coeff: 2 Live: - coeff: 0.4 + coeff: 0.5 Wheel: - coeff: 1 + coeff: 15 Mimic: coeff: 0 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py index 291157427..1f9381f59 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py @@ -1,5 +1,5 @@ from unitree_api import robot as rbt -from unitree_deploy.sine_generator import sine_generator +from unitree_deploy.angle_utils import sine_generator import signal import sys a1 = rbt.Robot() diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 35be8ad9f..a557fd8cf 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -6,7 +6,7 @@ from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO -from raisimGymTorch.env.deploy.angle_utils import transfer +from raisimGymTorch.env.deploy.angle_utils import transfer,get_last_position import os import math @@ -16,8 +16,8 @@ import torch import datetime import argparse -from unitree_deploy.sine_generator import sine_generator - +from unitree_deploy.angle_utils import sine_generator +from unitree_utils.Waiter import Waiter # task specification @@ -137,6 +137,7 @@ average_dones = 0. if update % cfg['environment']['eval_every_n'] == 0: + waiter = Waiter(0.01) print("Visualizing and evaluating the current policy") torch.save({ 'actor_architecture_state_dict': actor.architecture.state_dict(), @@ -151,24 +152,34 @@ env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') envs_idx = [0] * num_envs - + waiter.update_start() for step in range(n_steps): with torch.no_grad(): + waiter.wait() frame_start = time.time() obs = env.observe(False) + + # history_act = get_last_position(obs) + # history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) + + action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) action = action.cpu().detach().numpy() sine = sine_generator(envs_idx, schedule, angle_rate) action = transfer(action, sine, act_rate, history_act=history_act).astype(np.float32) reward, dones = env.step(action) + + history_act = sine + # envs_idx = list(map(check_done, envs_idx, dones)) + history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) + envs_idx = list(map(check_done, envs_idx, dones)) - history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) reward_analyzer.add_reward_info(env.get_reward_info()) frame_end = time.time() wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) - if wait_time > 0.: - time.sleep(wait_time) + # if wait_time > 0.: + # time.sleep(wait_time) env.stop_video_recording() env.turn_off_visualization() @@ -186,6 +197,8 @@ 1. z轴方向的加速度的处理方法 """ + # history_act = get_last_position(obs) + action = ppo.act(obs) @@ -193,6 +206,7 @@ action = transfer(action, sine, act_rate, history_act=history_act).astype(np.float32) # todo the transfer has bug reward, dones = env.step(action) + history_act = sine envs_idx = list(map(check_done, envs_idx, dones)) history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 52620ab9e..5624adfcf 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -18,7 +18,7 @@ import argparse # import pandas as pd # from sine_generator import sine_generator -from unitree_deploy.sine_generator import sine_generator +from unitree_deploy.angle_utils import sine_generator """" todo 1. left-hand right-hand check @@ -160,7 +160,7 @@ if moving_robot: from robot_utils import * - + a1.torque_limit = 33.15 init_robot(dt) ori_posi = sine_generator(0, schedule, rate=angle_rate).tolist() # initial position @@ -175,6 +175,9 @@ real_idx = 0 for i in range(schedule): a1.hold_on() + a1.kp = [100] * 12 + a1.kd = [7] * 12 + a1.init_k(a1.kp, a1.kd) for step in range(n_steps * 10): if not moving_robot: @@ -221,7 +224,7 @@ print('b-trans') real_action = transfer(gen_action, real_sine, act_rate, history_act=real_history_act) # real_action = real_action - print(f"real_obs:{real_obs} \n gen_action : {gen_action}\n for work_action:{real_action}") + print(f"real_obs:{real_obs} \n gen_action : {gen_action}\n for work_action:{real_action} \n now_tau_est:{a1.tau}") # waiter.wait() if real_action.shape[0] == 1: real_action = real_action[0] @@ -236,7 +239,7 @@ waiter.wait() reward, dones = env.step(action) print('exec') - envs_idx = list(map(check_done, envs_idx, dones)) + # envs_idx = list(map(check_done, envs_idx, dones)) history_act = sine envs_idx = list(map(check_done, envs_idx, dones)) history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) From edb15c70a9fef8ffa643d7cbd0197b8996bdef26 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Thu, 17 Aug 2023 21:04:04 +0800 Subject: [PATCH 23/62] saver drawer --- examples/src/server/go1.cpp | 64 ++- .../raisimGymTorch/deploy_log/anay.py | 22 +- .../raisimGymTorch/deploy_log/csv_saver.py | 26 ++ .../raisimGymTorch/deploy_log/draw_map.py | 77 ++++ .../raisimGymTorch/env/RewardAnalyzer.py | 26 +- .../env/VectorizedEnvironment.hpp | 4 +- .../raisimGymTorch/env/auto_runner.py | 2 +- .../raisimGymTorch/env/deploy/angle_utils.py | 59 +-- .../raisimGymTorch/env/deploy/onnx_deploy.py | 394 ++++++++++++------ .../envs/rsg/Environment_tobuildforpython.hpp | 97 +++-- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 32 +- .../env/envs/rsg/robot_utils.py | 2 + .../raisimGymTorch/env/envs/rsg/runner.py | 52 ++- .../env/envs/rsg/sim_with_real.py | 124 ++++-- 14 files changed, 686 insertions(+), 295 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/deploy_log/csv_saver.py create mode 100644 raisimGymTorch/raisimGymTorch/deploy_log/draw_map.py diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index b6cd51418..09abd91a8 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -79,6 +79,13 @@ void swap(Eigen::VectorXd &ob, int a, int b) // //} +void rot2euler( const raisim::Mat<3,3>& mat, Eigen::Vector3d& euler) +{ + euler[0] = atan2(mat[2], mat[8]); + euler[1] = asin(- mat[5]); + euler[2] = atan2(mat[3], mat[4]); +} + void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) { // std::cout<< "size is "<< angle_list.size() << std::endl; @@ -125,14 +132,14 @@ void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1 } int main(int argc, char* argv[]) { auto binaryPath = raisim::Path::setFromArgv(argv[0]); - + char c; /// create raisim world raisim::World world; world.setTimeStep(0.01); /// create objects auto ground = world.addGround(); - auto go1 = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\a1\\urdf\\a1.urdf"); + auto go1 = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\a1_description\\urdf\\a1.urdf"); /// go1 joint PD controller Eigen::VectorXd jointNominalConfig(go1->getGeneralizedCoordinateDim()), jointVelocityTarget(go1->getDOF()); @@ -175,9 +182,36 @@ int main(int argc, char* argv[]) { raisim::Vec<3> acc; Eigen::Vector3d ma; ma.setZero(); - for (int i=0; i<2000000; i++) { + Eigen::VectorXd bodyLinearVelocity, bodyAngularVelocity; + bodyAngularVelocity.setZero(3); + bodyLinearVelocity.setZero(3); + auto roott = go1->getBodyIdx("base"); +// auto imu = go1->getSensor("imu_joint"); + + for (int i=0; i<2000000; i++) { + + std::cin.ignore(); +// imu->getAngularVelocity() + + go1->getState(gc,gv); -// raisim::Vec<4> qu{1+double(i)/500 , 0, 0, 0 + double(i)/500}; + + +// anymal_->getState(gc, gv); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc[3]; quat[1] = gc[4]; quat[2] = gc[5]; quat[3] = gc[6]; + quat /= quat.norm(); + raisim::quatToRotMat(quat, rot); + + bodyLinearVelocity = rot.e().transpose() * gv.segment(0, 3); + bodyAngularVelocity = rot.e().transpose() * gv.segment(3, 3); + Eigen::Vector3d eule; + rot2euler(rot, eule); + std::cout<<"euler " << bodyAngularVelocity< qu{1+double(i)/500 , 0 , 0, 0+ double(i) / 500 }; // go1->setBaseOrientation(qu); Eigen::VectorXd tmp1(12), tmp2(12); tmp1 = gc.tail(12); @@ -203,16 +237,30 @@ int main(int argc, char* argv[]) { usleep(5000); angle_generator(position, i, 40, 0.35); jointNominalConfig.tail(12) = position; + for(auto n: go1->getBodyNames()) + { + std::cout << n << std::endl; + } + Eigen::Quaterniond quata(gc[3], gc[4], gc[5], gc[6]); + Eigen::Vector3d angle = ToEulerAngles(quata); + Eigen::Matrix3d root(quata); - Eigen::Quaterniond quat(gc[3], gc[4], gc[5], gc[6]); - Eigen::Vector3d angle = ToEulerAngles(quat); + bodyLinearVelocity = root.transpose() * gv.segment(0, 3); + bodyAngularVelocity = root.transpose() * gv.segment(3, 3); if (abs(angle[0]) >abs(ma[0]) ) ma[0] = abs(angle[0]); if (abs(angle[1]) >abs(ma[1]) ) ma[1] = abs(angle[1]); if (abs(angle[2]) >abs(ma[2]) ) ma[2] = abs(angle[2]); - + raisim::Vec<3> angvel; + go1->getAngularVelocity(roott, angvel); // std::cout<printOutFrameNamesInOrder(); + go1->getFrameAcceleration("floating_base", angvel); + std::cout<getAngularVelocity(); RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) // std::cout<< ob_ << std::endl< &reward, Eigen::Ref &done) { reward[agentId] = environments_[agentId]->step(action.row(agentId)); - rewardInformation_[agentId] = environments_[agentId]->getRewards().getStdMap(); float terminalReward = 0; done[agentId] = environments_[agentId]->isTerminalState(terminalReward); + rewardInformation_[agentId] = environments_[agentId]->getRewards().getStdMap(); if (done[agentId]) { environments_[agentId]->reset(); - reward[agentId] += terminalReward; +// reward[agentId] += terminalReward; } } diff --git a/raisimGymTorch/raisimGymTorch/env/auto_runner.py b/raisimGymTorch/raisimGymTorch/env/auto_runner.py index 456a2161c..c655590a6 100644 --- a/raisimGymTorch/raisimGymTorch/env/auto_runner.py +++ b/raisimGymTorch/raisimGymTorch/env/auto_runner.py @@ -11,7 +11,7 @@ def change_data(path, flag, data): with open(path, 'w') as f : dump(y, f, Dumper=RoundTripDumper) -test_list = [x/10 for x in range(1,20,4)] +test_list = [x/100 for x in range(4,20,4)] test_T = [40] # print(test_list) length = 500 diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py index aaca1b211..2b6a31acf 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py @@ -1,6 +1,7 @@ from math import sin,pi import numpy as np # from onnx_deploy import rad_deg, deg_rad +from raisimGymTorch.deploy_log.draw_map import Drawer def deg_rad(x): if isinstance(x, list): return [deg_rad(a) for a in x] @@ -11,7 +12,7 @@ def rad_deg(x): if isinstance(x, list): return [rad_deg(a) for a in x] else: - return x * pi / 180 + return x / pi * 180 low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] @@ -63,19 +64,49 @@ def transfer(act_gen, sine, k, history_act = None) ->np.array: """ # act_gen = (act_gen + 1) /2 # 0-1 act_gen = act_gen * bund # - + + # print(act_gen) act_gen = np.clip(act_gen, low, upp) if history_act is not None: - kk = 0.6 + kk = 0.9 # act_gen = (1-kk) * act_gen act_gen = act_gen * (1-kk) + history_act * kk act_gen = np.clip(act_gen, low, upp) if act_gen.shape[0] == 1: act_gen = act_gen[0] - action = act_gen * k + sine * (1-k) + action = act_gen * k + sine # print(np.abs(act_gen).max(), sine.max()) action = np.clip(action, low, upp) return action +def transfer_f(act_gen, sine, k, history_act = None) ->np.array: + """ + clip, lerp, generate the final action + params: act_gen: [12 * (-1, 1)] np.array + params: sine: [12 * rad_target] np.array + """ + # act_gen = (act_gen + 1) /2 # 0-1 + act_gen = act_gen * bund / 2 # - + + # print(act_gen) + act_gen = np.clip(act_gen, low, upp) + if history_act is not None: + kk = 1-k + # act_gen = (1-kk) * act_gen + # print(f'his1 {history_act}\n act_gen{act_gen}') + history_act = act_gen * (1-kk) + history_act * kk + history_act = np.clip(history_act, low, upp) + # print(f'his2 {history_act}') + + if history_act.shape[0] == 1: + tmp = history_act[0] + else: + tmp = history_act + action = tmp * 0.2 + sine + # print(np.abs(act_gen).max(), sine.max()) + action = np.clip(action, low, upp) + # print(f'bound{bund}\n act_gen{act_gen}\n history_act{history_act}\n sine{sine} \naction{action}\nk{k} ') + # input('1') + return action, history_act + def get_last_position(obs): if isinstance(obs, list): x = [obs[-2 * i] for i in range(1, 13)] @@ -89,28 +120,6 @@ def get_last_position(obs): return np.stack(x, axis=1) if __name__=='__main__': - # angle_list = [0 for x in range(12)] - # - # sine_generator(angle_list, 0, 40, 0.3) - # angle_list = np.array(angle_list) - # act = transfer(np.zeros((100,12)), angle_list, 1) - # print('sine:', angle_list) - # print('low:', low) - # print('upp:', upp) - # print('bound', bund) - # print(act[0]) - # his_util = [0, 0.523, -1.046] * 4 - # check_done = lambda a, b: a + 1 if not b else 0 - # check_history = lambda a, b: a if not b else his_util - # check_zero = lambda a:1 if a!=0 else 0 - # tmp1 = np.zeros((100,12)) - # idx = np.random.rand(100) - # idx[1:10] = False - # idx = list(map(check_zero, idx)) - # print(idx) - # tmp = np.array([check_history(tmp1[i], idx[i]) for i in range(100) ]) - # assert tmp1.shape == tmp.shape - # print(tmp) a = np.random.rand(2,30) print(a) print(get_last_position(a).shape) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index eb1b17997..bf27f3387 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -3,19 +3,22 @@ # from mlprodict.onnxrt import OnnxInference from math import cos,pi import numpy as np -from angle_utils import deg_rad, rad_deg +from raisimGymTorch.env.deploy.angle_utils import deg_rad, rad_deg +from raisimGymTorch.deploy_log.draw_map import Drawer # 加载模型 def ang_trans(lower, upper, x): """ - trans the rate x to the angle between lower and upper + trans the rate x to the angle """ x = (x+1)/2 #todo for test return x * upper + (1-x) * lower def norm(lower, upper, x): + # print(lower, upper, x) + assert abs((x - lower) / (upper - lower) ) <= 1 return (x - lower) / (upper - lower) def rad_normalize(lower, upper,x): @@ -30,6 +33,9 @@ def rad_normalize(lower, upper,x): return 2*ang1 - 1 def deg_normalize(lower, upper, x): + """ + make angle to rate from -1 -- 1 + """ if isinstance(lower, list): ans = [] for a,b,c in zip(lower, upper, x): @@ -43,11 +49,18 @@ def deg_normalize(lower, upper, x): low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] u0 = [0, 30, -60,0, 30, -60,0, 30, -60,0, 30, -60] +# u0_rad = deg_rad(u0) +# u0 = [0] * 12 # todo sure ? u0 = 0 +# low_rad = deg_rad(low) +# upp_rad = deg_rad(upp) u0 = deg_normalize(low, upp, u0) +# u0_rad = rad_normalize(low_rad, upp_rad, u0_rad) # u0 = [-deg_normalize(low, upp, u0)[i] if deg_normalize(low, upp, u0)[i]!=0 else 0 for i in range(12)] # print(u0) -history_u = u0 -model = ort.InferenceSession('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/deploy/new.onnx') +history_u = [0] * 12 +# history_u_rad = [0] * 12/ +# history_u = u0.copy() +model = ort.InferenceSession('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/deploy/walk.onnx') idx_local = 0 @@ -58,66 +71,78 @@ def deg_normalize(lower, upper, x): print(input_name, output_name) # input_data = np.random.rand(1, 34).astype(np.float32) -def sine_gene_test_qua(idx, T): - angle_list = [0 for i in range(12)] - if idx >= 2 * T: - idx = 0 - dh = 1 # todo for test - if idx >= 0 and idx <= T: - tp0 =idx - 0 - y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - y2 = 0 - elif idx>T and idx<=2*T: - tp0 =idx - T - y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - y1 = 0 - - - angle_list[0] = 0 - angle_list[1] = y1 - angle_list[2] = -2 * y1 - angle_list[3] = 0 - angle_list[4] = y2 - angle_list[5] = -2 * y2 - angle_list[6] = 0 - angle_list[7] = y1 - angle_list[8] = -2 * y1 - angle_list[9] = 0 - angle_list[10] = y2 - angle_list[11] = -2 * y2 - angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i])) for i in range(12)] - return angle_list +# def sine_gene_test_qua(idx, T): +# angle_list = [0 for i in range(12)] +# if idx >= 2 * T: +# idx = 0 +# dh = 1.2 # todo for test +# if idx >= 0 and idx <= T: +# tp0 =idx - 0 +# y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 +# y2 = 0 +# elif idx>T and idx<=2*T: +# tp0 =idx - T +# y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 +# y1 = 0 +# +# +# angle_list[0] = 0 +# angle_list[1] = y1 +# angle_list[2] = -2 * y1 +# angle_list[3] = 0 +# angle_list[4] = y2 +# angle_list[5] = -2 * y2 +# angle_list[6] = 0 +# angle_list[7] = y1 +# angle_list[8] = -2 * y1 +# angle_list[9] = 0 +# angle_list[10] = y2 +# angle_list[11] = -2 * y2 +# angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i])) for i in range(12)] +# return angle_list def sine_gene(idx, T): - angle_list = [0 for i in range(12)] - if idx >= 2 * T: - idx = 0 - dh = 2 # todo for test - if idx >= 0 and idx <= T: - tp0 =idx - 0 - y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - y2 = 0 - elif idx>T and idx<=2*T: - tp0 =idx - T - y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - y1 = 0 - - - angle_list[0] = 0 - angle_list[1] = y1 - angle_list[2] = -2 * y1 - angle_list[3] = 0 - angle_list[4] = y2 - angle_list[5] = -2 * y2 - angle_list[6] = 0 - angle_list[7] = y2 - angle_list[8] = -2 * y2 - angle_list[9] = 0 - angle_list[10] = y1 - angle_list[11] = -2 * y1 - angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i])) for i in range(12)] - return angle_list + # if isinstance(idx, np.ndarray): + # + if not isinstance(idx, list): + angle_list = [0 for i in range(12)] + if idx >= 2 * T: + idx = 0 + dh = 0.8 # todo for test + if idx >= 0 and idx <= T: + tp0 =idx - 0 + y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y2 = 0 + elif idx>T and idx<=2*T: + tp0 =idx - T + y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y1 = 0 + + + angle_list[0] = 0 + angle_list[1] = y1 + angle_list[2] = -2 * y1 + angle_list[3] = 0 + angle_list[4] = y2 + angle_list[5] = -2 * y2 + angle_list[6] = 0 + angle_list[7] = y2 + angle_list[8] = -2 * y2 + angle_list[9] = 0 + angle_list[10] = y1 + angle_list[11] = -2 * y1 + # print("ang_list ", angle_list) + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i]) ) for i in range(12)] + # print("ang_list ", angle_list) + return angle_list + else: + ans = [] + for i in idx: + ans.append(sine_gene(i, T)) + return ans + + def rate_to_act(lower, upper, rate): if isinstance(lower, list): @@ -125,48 +150,151 @@ def rate_to_act(lower, upper, rate): else: norm_for_act = lambda a: (a+1)*0.5 lerp = lambda a, b, c: a*(1-c) + b * c + # print("11111 ", lower, upper, rate, lerp(lower, upper, norm_for_act(rate))) + return lerp(lower, upper, norm_for_act(rate)) -def add_list(act_gen, sine): - global history_u +def clip(a,b,c): + # print('before clip ', a, b, c) + + if isinstance(a, np.ndarray): + return np.clip(a,b,c) + + if a <= b : + a = b + if a >= c: + a = c + # print('clip ', a) + return a + +def add_list(act_gen, sine, history=None): + if isinstance(act_gen, np.ndarray): + # print(act_gen) + assert act_gen.shape[0] == 12 + else: + # print(act_gen) + assert len(act_gen) == 12 kk = 0.9 kf = 1 - kb = 0.5 - history_u = [history_u[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big - clip = lambda a, b, c: np.clip(a, b, c) + kb = 0.2 + if history is None: + global history_u + + # print(history_u, act_gen) + history_u = [history_u[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big + + ans = [rate_to_act(low[i], upp[i], clip(kb * history_u[i] + kf * sine[i], -1, 1)) for i in range(12)] + ans = [deg_rad(x) for x in ans] + # todo swap the ans + + return ans + else: + # history_u = history + history = [history[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big + + ans = [rate_to_act(low[i], upp[i], clip(kb * history[i] + kf * sine[i], -1, 1)) for i in range(12)] + ans = [deg_rad(x) for x in ans] + # todo swap the ans + + return ans, history + + +def run_model_with_pt_input(act_gen, idx, T, history): + if isinstance(idx, list): + idx = [i%(2*T) for i in idx] + else: + idx = idx % (2 * T) + # act_gen = act_gen/3.14 * 180 + # act_gen = np.zeros_like(act_gen) + sine = sine_gene(idx, T) + ans = [] + new_his = [] + for i in range(act_gen.shape[0]): + tmp, tmp_his= add_list(act_gen[i], sine[i], history[i]) + ans.append(tmp) + new_his.append(tmp_his) + + return np.array(ans).astype(np.float32),np.array(new_his).astype(np.float32) + +def run_model_with_pt_input_modify(act_gen, idx, T, history): + if isinstance(idx, list): + idx = np.array(idx) + print(idx.shape) + idx = idx%(2*T) + else: + idx = idx % (2 * T) + + sine = sine_gene(idx, T) + ans = [] + new_his = [] + for i in range(act_gen.shape[0]): + tmp, tmp_his= add_list(act_gen[i], sine[i], history[i]) + ans.append(tmp) + new_his.append(tmp_his) + return np.array(ans).astype(np.float32),np.array(new_his).astype(np.float32) - # f0 = lambda a: a - # f1 = lambda a, b, c: a - ans = [rate_to_act(low[i], upp[i], clip(kb * history_u[i] + kf * sine[i], -1, 1)) for i in range(12)] - ans = [deg_rad(x) for x in ans] - # todo swap the ans - # for i in range(6): - # tmp = ans[i+6] - # ans[i+6] = ans[i] - # ans[i] = tmp - return ans +def list_pt(act_gen, idx, T): + ans = [] + for i in range(act_gen.shape[0]): + ans.append(run_model_with_pt_input(act_gen[i, :], idx[i], T)) + ans = np.vstack(ans).astype(np.float32) + return ans -def run_model(observation, idx, T): +def run_model(observation, idx, T, save_gen=None): # todo idx should return 0 when fall - # idx = idx % T + idx = idx % (2 * T) + # print(observation.shape) + # obs = rad_deg(observation) + observation[:, [0,1]] = observation[:, [1,0]] + observation[:, [1]] = - observation[:, [1]] + + # observation[:,[2,3,4]] = rad_deg(observation[:, [2,3,4]]) + # observation[:,[2,3,4]] = observation[:, [2,3,4]] + observation[:,[2,3,4]] = observation[:, [3,4,2]] + # observation[:,[2,3,4]] = rad_deg(observation[:, [3,4,2]]) + observation[:, [3, 4]] = - observation[:, [3, 4]] + # observation[:, [4] + if observation.shape[1] == 29: + offset = 0 + elif observation.shape[1] == 26: + offset = 3 + else: + offset = 5 + print('offset' ,offset) + be_obe =[5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] + aft_obs = [23,24,25,26,27,28,17,18,19,20,21,22,11,12,13,14,15,16,5,6,7,8,9,10] + be_obe = [i -offset for i in be_obe] + aft_obs=[i -offset for i in aft_obs] + # print(be_obe, aft_obs) + observation[: ,be_obe]=\ + observation[: ,aft_obs] + + act_gen = model.run([output_name], input_feed={input_name: observation}) act_gen = act_gen[0] - act_gen = np.zeros((1,12)) - # [array([[]])] + + # minu = np.array([0, 0, deg_rad(30), 0, deg_rad(-60), 0] * 4) # 6 + # observation[:, -24:] -= minu + + # act_gen = np.zeros((1,12)) + if save_gen is not None: + save_gen.add_list(act_gen[0]) + sine = sine_gene(idx, T) - sine = sine_gene_test_qua(idx, T) - # sine = [0 for x in range(12)] - # print(sine,act_gen) + ans = [] for act in act_gen: ans.append(add_list(act, sine)) + ans = np.array(ans).astype(np.float32) + # be_ans = + ans[:, [0,1,2,3,4,5,6,7,8,9,10,11]] = \ + ans[:, [9,10,11,6,7,8,3,4,5,0,1,2]] + ans = [ans] - ans = [np.array(ans).astype(np.float32)] - - return ans + return ans, observation def run_model1(observation): @@ -178,63 +306,55 @@ def run_model1(observation): # print(outputs) # x = run_model(input_data) # print(x) -if __name__=="__main__": - # test_exp = np.zeros((1,34)) - # test_exp1 = np.zeros((1,34)) - # for i in range(12): - # test_exp[0][i * 2 + 10] = -u0[i] - # test_exp1[0][i * 2 + 10] = u0[i] - # # print(test_exp) - # - # ans = run_model(test_exp.astype(np.float32),0, 50) - # ans1 = run_model(test_exp1.astype(np.float32),0, 50) - # # ans1 = run_model(np.zeros((1,34)).astype(np.float32)) - # print(ans) - # print(ans1) - # print(u0) - - a = [ang_trans(low[i], upp[i], u0[i]) for i in range(12)] - print(a) - print(u0) - uu = rate_to_act(low, upp, u0) - uu = deg_rad(uu) - print(uu) - # norm_for_act = lambda a: (a + 1) * 0.5 - # lerp = lambda a, b, c: a * (1 - c) + b * c - # print(deg_normalize(-10,10,6)) - # print(norm_for_act(deg_normalize(-10,10,6))) - # print(lerp(-10, 10,norm_for_act(deg_normalize(-10,10,6)) )) - - # import matplotlib.pyplot as plt - # x = [x for x in range(200)] - # y = [sine_gene(a ,50) for a in x] - # y =np.array(y).transpose() + + + # if not isinstance(idx, list): + # angle_list = [0 for i in range(12)] + # if idx >= 2 * T: + # idx = 0 + # dh = 0.8 # todo for test + # if idx >= 0 and idx <= T: + # tp0 =idx - 0 + # y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + # y2 = 0 + # elif idx>T and idx<=2*T: + # tp0 =idx - T + # y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + # y1 = 0 # - # for i in range(12): # - # plt.plot(x,y[i]) - # plt.show() - -""" -def test_rad_deg(): - assert rad_deg(1.57)-90 <=1 -def test_rad_deg_list(): - assert isinstance(rad_deg([1, 2,3]), list) + # angle_list[0] = 0 + # angle_list[1] = y1 + # angle_list[2] = -2 * y1 + # angle_list[3] = 0 + # angle_list[4] = y2 + # angle_list[5] = -2 * y2 + # angle_list[6] = 0 + # angle_list[7] = y2 + # angle_list[8] = -2 * y2 + # angle_list[9] = 0 + # angle_list[10] = y1 + # angle_list[11] = -2 * y1 + # # print("ang_list ", angle_list) + # angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i]) ) for i in range(12)] + # # print("ang_list ", angle_list) + # return angle_list + +def sine_gen(idx, T): + """ + idx : 0~ 2*T + iter ? + """ + angle_list = np.zeros((idx.shape[0], 12)) -def test_deg_rad(): - assert deg_rad(90) - 1.57 <= 0.05 -def test_deg_rad_list(): - assert isinstance(deg_rad([1, 2, 3]), list) -""" -# def test_sine_gene(): -# for i in range(0, 200, 10): -# a = sine_gene(i, 50) -# print(a) -# assert max(a) <= pi and min(a) >= 0 -# todo +if __name__=="__main__": + # sine_gen(np.zeros(100), 40) + mat = np.zeros((100,12)) + mat = np.where(np.indices(mat)>20, 1, 0) + print(mat) """ todo 1. on action 的范围 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index e2e6ea184..38caead50 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -70,13 +70,14 @@ namespace raisim { angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); acc_.setZero();//1 + ang_vel_.setZero(); // init_position(); gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; // init_position(gc_init_); init(); - obDim_ = 30; + obDim_ = 29; actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); obDouble_.setZero(obDim_); @@ -175,15 +176,15 @@ namespace raisim { double rrr =0; // for(int i=0;i<=2;i++) rrr += abs(euler_angle[i]) ; rrr = abs(euler_angle[0]) + abs(euler_angle[1]); - rrr = abs(gc_[0]) + abs(gc_[1]); +// rrr = abs(gc_[0]) + abs(gc_[1]); // rrr += (gc_.tail(12) - pTarget12_).norm() ; bool accu = false; - rewards_.record("Stable", - rrr, accu); + rewards_.record("Stable",-rrr, accu); rewards_.record("Live", 1, accu); - rewards_.record("forwardVel", bodyLinearVel_[0], accu); - rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); +// rewards_.record("forwardVel", bodyLinearVel_[0], accu); +// rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); - rewards_.record("Wheel", euler_angle[2] * 0.1 + (bodyAngularVel_[2] * 1), accu); + rewards_.record("Wheel", 0.3 - abs(ang_vel_[2]- 0.3), accu); euler_angle_old = euler_angle; // rewards_.record("torque", ) return rewards_.sum(); @@ -198,6 +199,7 @@ namespace raisim { raisim::quatToRotMat(quat, rot); bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); + anymal_->getAngularVelocity(anymal_->getBodyIdx("base"), ang_vel_); Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); euler_angle = ToEulerAngles(qua); // if(euler_angle[1] > PI) euler_angle[1] -= 2*PI; @@ -213,12 +215,16 @@ namespace raisim { c_v[i*2] = gcc[i]; c_v[i*2 + 1] = gvv[i]; } - obDouble_ << euler_angle[0], + + obDouble_ << + euler_angle[0], euler_angle[1],// quaternion - bodyAngularVel_[0], // ras/s - bodyAngularVel_[1], // ras/s - bodyAngularVel_[2], // ras/s - c_v; + ang_vel_[0], + ang_vel_[1], + ang_vel_[2], + c_v; +// std::cout<<"ang_vel : " << ang_vel_ << std::endl; + } @@ -229,36 +235,45 @@ namespace raisim { bool isTerminalState(float& terminalReward) final { terminalReward = float(terminalRewardCoeff_); - for(auto& contact: anymal_->getContacts()) - if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) - {// if there is any contact body was not in the footIndices the over - rewards_.record("Live", -10, false); - return true;} - if(abs(gc_[2] - gc_init_[2]) > 0.3){ return true;} - +// for(auto& contact: anymal_->getContacts()) +// if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) +// {// if there is any contact body was not in the footIndices the over +// rewards_.record("Live", -10, false); +// return true;} + if(abs(gc_[2] - gc_init_[2]) > 0.22){ +// std::cout<<"z done" << std::endl; + rewards_.record("Live", terminalReward, false); - if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.2) - { -// std::cout<<"y angle done" << std::endl; - return true; - } - if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.2) - { -// std::cout<<"x angle done" << std::endl; - return true; - } - if (abs(gc_[0] - gc_init_[0]) >= 0.4) - { - return true; - } - if (abs(gc_[1] - gc_init_[1]) >= 0.4) - { - return true; - } - if(euler_angle[2] < -0.3) - { - return true; + return true; } + + +// if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.17) +// { +//// std::cout<<"y angle done" << std::endl; +// rewards_.record("Live", terminalReward, false); +// +// return true; +// } +// if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.17) +// { +//// std::cout<<"x angle done" << std::endl; +// rewards_.record("Live", terminalReward, false); +// +// return true; +// } +// if (abs(gc_[0] - gc_init_[0]) >= 0.4) +// { +// return true; +// } +// if (abs(gc_[1] - gc_init_[1]) >= 0.4) +// { +// return true; +// } +// if(euler_angle[2] < -0.3) +// { +// return true; +// } terminalReward = 0.f; return false; } @@ -270,9 +285,9 @@ namespace raisim { bool visualizable_ = false; raisim::ArticulatedSystem* anymal_, *anymal_1; Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; - double terminalRewardCoeff_ = -10.; + double terminalRewardCoeff_ = -100.; Eigen::VectorXd actionMean_, actionStd_, obDouble_; - Vec<3> acc_; + Vec<3> acc_, ang_vel_; Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; std::set footIndices_; double p_gain,d_gain; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 167540e16..996b3d834 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -5,36 +5,36 @@ environment: render: true # just testing commenting num_envs: 100 - eval_every_n: 50 + eval_every_n: 100 num_threads: 30 simulation_dt: 0.0025 control_dt: 0.01 max_time: 4 - action_std: 0.03 + action_std: 0.5 # action_std: 0 show_ref: false - angle_rate: 0.2 + angle_rate: 0.21 schedule: 40 - for_work: 1 +# for_work: 1 float_base: false - learnning_rate: 1e-3 + learnning_rate: 5e-4 p_gain: 180 d_gain: 8 urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: - forwardVel: - coeff: 0 - torque: - coeff: -4e-5 +# forwardVel: +# coeff: 0 +# torque: +# coeff: -4e-5 Stable: - coeff: 2 + coeff: 0.4 Live: - coeff: 0.5 + coeff: 1 Wheel: - coeff: 15 - Mimic: - coeff: 0 + coeff: 0.3 +# Mimic: +# coeff: 0. architecture: - policy_net: [512, 512] - value_net: [512, 512] + policy_net: [128, 128] + value_net: [128, 128] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py index 1f9381f59..4ce308a58 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/robot_utils.py @@ -1,5 +1,6 @@ from unitree_api import robot as rbt from unitree_deploy.angle_utils import sine_generator +# from raisimGymTorch.deploy_log.draw_map import draw import signal import sys a1 = rbt.Robot() @@ -12,6 +13,7 @@ def init_position(position, timing = 100): def quit_robot(robot): robot.back_safe() print('Task finished') + # draw() sys.exit(0) def signal_handler(signal, frame): diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index a557fd8cf..685b47833 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -6,8 +6,8 @@ from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO -from raisimGymTorch.env.deploy.angle_utils import transfer,get_last_position - +from raisimGymTorch.env.deploy.angle_utils import transfer_f ,get_last_position +from raisimGymTorch.env.deploy.onnx_deploy import run_model_with_pt_input_modify, list_pt import os import math import time @@ -18,6 +18,7 @@ import argparse from unitree_deploy.angle_utils import sine_generator from unitree_utils.Waiter import Waiter +from raisimGymTorch.deploy_log.draw_map import Drawer # task specification @@ -111,7 +112,8 @@ if mode == 'retrain': load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) -his_util=[0, 0.523, -1.046] * 4 +# his_util=[0, 0.523, -1.046] * 4 +his_util=[0] * 12 check_done = lambda a, b: a + 1 if not b else 0 check_history = lambda a, b: a if not b else his_util # need_to_review = lambda a,b: a if not b else np.array([[0, 0.523, -1.046] * 4]) # todo make sure the shape @@ -121,7 +123,7 @@ print = logger.info mode == 'train' -history_act = np.array([his_util] * 100) +history_act = np.array([his_util] * num_envs) total_update = args.update if mode =='train' or mode == 'retrain': # print('start train') @@ -149,6 +151,9 @@ loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + # debug_draw_action = Drawer('debug_draw_action') + # debug_draw_history = Drawer('debug_draw_history') + env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') envs_idx = [0] * num_envs @@ -158,6 +163,7 @@ waiter.wait() frame_start = time.time() obs = env.observe(False) + # print(obs.shape) # history_act = get_last_position(obs) # history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) @@ -165,12 +171,23 @@ action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) action = action.cpu().detach().numpy() - sine = sine_generator(envs_idx, schedule, angle_rate) - action = transfer(action, sine, act_rate, history_act=history_act).astype(np.float32) + + # sine = sine_generator(envs_idx, schedule, angle_rate) + # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) + # action = action.astype(np.float32) + # + + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + + + # debug_draw_action.add_map_list(action[0]) + # debug_draw_history.add_map_list(history_act[0]) + reward, dones = env.step(action) - history_act = sine + # history_act = sine # envs_idx = list(map(check_done, envs_idx, dones)) + # history_act = action history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) envs_idx = list(map(check_done, envs_idx, dones)) @@ -183,11 +200,14 @@ env.stop_video_recording() env.turn_off_visualization() - + history_act = np.array([his_util] * num_envs) reward_analyzer.analyze_and_plot(update) env.reset() env.save_scaling(saver.data_dir, str(update)) + # debug_draw_action.draw() + # debug_draw_history.draw() + # actual training # sine = [0] * 12 envs_idx = [0] * num_envs @@ -202,13 +222,19 @@ action = ppo.act(obs) - sine = sine_generator(envs_idx, schedule, angle_rate) - action = transfer(action, sine, act_rate, history_act=history_act).astype(np.float32) + # sine = sine_generator(envs_idx, schedule, angle_rate) + # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) + # action = action.astype(np.float32) + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + # todo the transfer has bug reward, dones = env.step(action) + # input('1') + # print(f"history : {history_act}") - history_act = sine - envs_idx = list(map(check_done, envs_idx, dones)) + # history_act = sine + # envs_idx = list(map(check_done, envs_idx, dones)) + # history_act = action history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) # reward = reward * envs_idx / n_steps * 2 @@ -217,6 +243,8 @@ ppo.step(value_obs=obs, rews=reward, dones=dones) done_sum = done_sum + np.sum(dones) reward_sum = reward_sum + np.sum(reward) + reward_analyzer.add_reward_info(env.get_reward_info()) + reward_analyzer.analyze_and_plot(update) # take st step to get value obs obs = env.observe(False) ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 5624adfcf..d009849a3 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -8,6 +8,9 @@ import raisimGymTorch.algo.ppo.ppo as PPO from raisimGymTorch.env.deploy.angle_utils import transfer from unitree_utils.Waiter import Waiter +from raisimGymTorch.env.deploy.angle_utils import get_last_position +from raisimGymTorch.deploy_log.draw_map import Drawer +from raisimGymTorch.deploy_log.csv_saver import CSV_saver import os import math import time @@ -19,6 +22,7 @@ # import pandas as pd # from sine_generator import sine_generator from unitree_deploy.angle_utils import sine_generator +from raisimGymTorch.env.deploy.angle_utils import deg_rad, rad_deg """" todo 1. left-hand right-hand check @@ -134,18 +138,20 @@ moving_robot = False +onnx_flag = True virtual = True +# his_util=[0, 0.523, -1.046] * 4 his_util=[0, 0.523, -1.046] * 4 # check_done = lambda a, b: a + 1 if not b else 0 check_history = lambda a, b: a if not b else his_util -history_act = np.array([his_util] * 100) +history_act = np.array([his_util] * num_envs) # init virtual part if virtual: waiter= Waiter(0.01) env.reset() start = time.time() - onnx_flag = False + onnx_flag = True if onnx_flag: cnt_onnx = 0 from raisimGymTorch.env.deploy import onnx_deploy @@ -159,6 +165,10 @@ if moving_robot: + history_obs = None + if onnx_flag: + cnt_onnx = 0 + from raisimGymTorch.env.deploy import onnx_deploy from robot_utils import * a1.torque_limit = 33.15 init_robot(dt) @@ -166,7 +176,7 @@ ori_posi = sine_generator(0, schedule, rate=angle_rate).tolist() # initial position # a1.kp = cfg['environment']['kp'] # ori_posi = a1.position - init_position(ori_posi,200) + init_position(ori_posi,250) print(f""" now_posi: {a1.position}, angle_rate : {angle_rate} @@ -176,73 +186,123 @@ for i in range(schedule): a1.hold_on() a1.kp = [100] * 12 - a1.kd = [7] * 12 + a1.kd = [3] * 12 a1.init_k(a1.kp, a1.kd) + history_obs = a1.observe()[ :5] -for step in range(n_steps * 10): - if not moving_robot: +for step in range(n_steps * 2): + #virtual bot part + if virtual: + # if step%1 ==0 : + # input() if step == 0: + draw_vir_obs = Drawer('vir_obs1') + draw_vir_action = Drawer('vir_sent_act') + draw_vir_recv_action = Drawer('vir_recv_act') waiter.update_start() - print('moving?') - time.sleep(0.005) + # save_gen_act = CSV_saver('virtual_gen_act', './') + save_obs = CSV_saver('virtual_obs', './') + save_for_work = CSV_saver('virtual_for_work', './') obs = env.observe(False) - - - #virtual bot part - if virtual: + print('saving') + last_p = get_last_position(obs) + print(f'last{last_p}') + draw_vir_recv_action.add_map_list(last_p[0]) if onnx_flag: - action = onnx_deploy.run_model(obs, cnt_onnx, 50) + # obs = rad_deg(obs) + # print(obs.shape) + action, obs = onnx_deploy.run_model(obs, cnt_onnx, 40) action = np.array(action)[0] cnt_onnx += 1 else: gen_action = ppo.act(obs) sine = sine_generator(envs_idx, schedule, angle_rate) action = transfer(gen_action, sine, act_rate, history_act=history_act).astype(np.float32) - print(f"virtual_obs:{obs} \n virtual_gen_action:{gen_action} \n virtual_action:{action}") + # history_act = + print(f"virtual_obs:{obs} \n virtual_action:{action}") + save_obs.add_list(obs[0]) + # draw_vir_recv_action.add_map_list(action[0]) + save_for_work.add_list(action[0]) - # obs = np.zeros((1,31)) - # gen_act = ppo.act(obs) - # sine = sine_generator(real_idx, schedule, angle_rate) - # act = transfer(gen_act, sine, act_rate) # real bot part if moving_robot: + if step ==0: + draw_real_obs = Drawer('real_obs') + draw_sent_action = Drawer('real_sent_act') + draw_recv_action = Drawer('real_a_recv_act') real_history_act = np.array(his_util) + save_real_obs = CSV_saver('real_a_obs') + save_real_action = CSV_saver('real_a_action') print('b-observe') - real_obs = a1.observe() + obbs = a1.observe() + obbs[:5] -= history_obs + real_obs = np.array([obbs]) # real_obs = np.array([a1.observe()]) print(f"real_obs {real_obs}") # input('now cancel it ') # print() print('b-ppo') - gen_action = ppo.act(real_obs) - print('b-sin') - real_sine = sine_generator(real_idx, schedule, angle_rate) - print('b-trans') - real_action = transfer(gen_action, real_sine, act_rate, history_act=real_history_act) + + + if onnx_flag: + action, real_obs = onnx_deploy.run_model(real_obs, cnt_onnx, 40) + real_obs = real_obs[0] + real_action = np.array(action)[0] + cnt_onnx += 1 + + else: + gen_action = ppo.act(real_obs) + print('b-sin') + real_sine = sine_generator(real_idx, schedule, angle_rate) + print('b-trans') + real_action = transfer(gen_action, real_sine, act_rate, history_act=real_history_act) + + + # real_action = real_action - print(f"real_obs:{real_obs} \n gen_action : {gen_action}\n for work_action:{real_action} \n now_tau_est:{a1.tau}") + # add_map_list(real_action) + draw_real_obs.add_map_list(real_obs[:5]) + save_real_obs.add_list(real_obs) + save_real_action.add_list(a1.position) + draw_recv_action.add_map_list(a1.position) + draw_sent_action.add_map_list(real_action[0]) + print(f"real_obs:{real_obs} \n for work_action:{real_action} ") # waiter.wait() if real_action.shape[0] == 1: real_action = real_action[0] a1.take_action(real_action.tolist()) - real_history_act = real_sine - # envs_idx = list(map(check_done, real_idx, dones)) - # history_act = np.array([check_history(history_act[i], 0) for i in range(num_envs)]) - real_idx += 1 + # real_history_act = real_sine + real_idx +=1 + # update virtual bot if virtual: waiter.wait() reward, dones = env.step(action) - print('exec') - # envs_idx = list(map(check_done, envs_idx, dones)) - history_act = sine envs_idx = list(map(check_done, envs_idx, dones)) + + draw_vir_obs.add_map_list(obs[0][:5]) + draw_vir_action.add_map_list(action[0]) history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) +# draw() +if virtual: + save_obs.save() + save_for_work.save() + draw_vir_obs.draw() + draw_vir_action.draw() + draw_vir_recv_action.draw() + # save_gen_act.save() +else: + a1.back_safe() + save_real_obs.save() + save_real_action.save() + draw_real_obs.draw() + draw_sent_action.draw() + draw_recv_action.draw() print(f'biggest:{biggest_reward},rate = {biggest_iter}') From 71055cedc8766ef53fa853e078c66f671163b469 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Thu, 17 Aug 2023 23:27:43 +0800 Subject: [PATCH 24/62] todo: 1. sine_line has a little different (recv) 2. train of the rotate --- examples/src/server/sensors.cpp | 1 + .../raisimGymTorch/env/deploy/onnx_deploy.py | 104 +++++++++++++----- .../envs/rsg/Environment_tobuildforpython.hpp | 28 ++--- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 4 +- .../raisimGymTorch/env/envs/rsg/runner.py | 8 +- .../env/envs/rsg/sim_with_real.py | 7 +- 6 files changed, 104 insertions(+), 48 deletions(-) diff --git a/examples/src/server/sensors.cpp b/examples/src/server/sensors.cpp index 41f814831..f4076bdf1 100755 --- a/examples/src/server/sensors.cpp +++ b/examples/src/server/sensors.cpp @@ -52,6 +52,7 @@ int main(int argc, char **argv) { for (int k = 0; k < loopN; k++) { RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) server.integrateWorldThreadSafe(); + std::cout << imu->getAngularVelocity(); } server.killServer(); diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index bf27f3387..c8f5039cc 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -1,3 +1,5 @@ +import time + import onnxruntime as ort # import onnx # from mlprodict.onnxrt import OnnxInference @@ -49,6 +51,8 @@ def deg_normalize(lower, upper, x): low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] u0 = [0, 30, -60,0, 30, -60,0, 30, -60,0, 30, -60] +low_np = np.array(low) +upp_np = np.array(upp) # u0_rad = deg_rad(u0) # u0 = [0] * 12 # todo sure ? u0 = 0 # low_rad = deg_rad(low) @@ -60,7 +64,7 @@ def deg_normalize(lower, upper, x): history_u = [0] * 12 # history_u_rad = [0] * 12/ # history_u = u0.copy() -model = ort.InferenceSession('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/deploy/walk.onnx') +model = ort.InferenceSession('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/deploy/2203.onnx') idx_local = 0 @@ -103,21 +107,32 @@ def deg_normalize(lower, upper, x): def sine_gene(idx, T): - # if isinstance(idx, np.ndarray): - # + # print(idx) + if isinstance(idx, np.ndarray) or isinstance(idx, list): + mat = np.zeros((len(idx), 12)) + i_set = set(idx) + idd = {} + for i in i_set: + idd[i] = sine_gene(i, 40) + for i in range(len(idx)): + mat[i] = idd[idx[i]] + return mat if not isinstance(idx, list): angle_list = [0 for i in range(12)] if idx >= 2 * T: idx = 0 - dh = 0.8 # todo for test + dh = 1.6 # todo for test + if idx >= 0 and idx <= T: tp0 =idx - 0 y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 y2 = 0 + y2 = y1 elif idx>T and idx<=2*T: tp0 =idx - T y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 y1 = 0 + y1 = y2 angle_list[0] = 0 @@ -176,28 +191,41 @@ def add_list(act_gen, sine, history=None): assert len(act_gen) == 12 kk = 0.9 kf = 1 - kb = 0.2 + kb = 0.4 if history is None: global history_u # print(history_u, act_gen) history_u = [history_u[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big + # print("11111", history_u, act_gen) ans = [rate_to_act(low[i], upp[i], clip(kb * history_u[i] + kf * sine[i], -1, 1)) for i in range(12)] ans = [deg_rad(x) for x in ans] # todo swap the ans - + # print(ans) + # input()/ return ans else: # history_u = history history = [history[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big - ans = [rate_to_act(low[i], upp[i], clip(kb * history[i] + kf * sine[i], -1, 1)) for i in range(12)] ans = [deg_rad(x) for x in ans] # todo swap the ans return ans, history +def lerp_np(a, b, c): + return a* (1-c) + b*c + +def add_list_np(act_gen, sine, history): + kk = 0.9 + kf = 1 + kb = 0.2 + history = history*kk + (1-kk) * act_gen + ans = np.clip(kb*history + kf * sine, -1, 1) + ans = (ans + 1) /2 # 100 * 12 + ans = lerp_np(low_np, upp_np, ans) + return ans, history def run_model_with_pt_input(act_gen, idx, T, history): if isinstance(idx, list): @@ -219,20 +247,18 @@ def run_model_with_pt_input(act_gen, idx, T, history): def run_model_with_pt_input_modify(act_gen, idx, T, history): if isinstance(idx, list): idx = np.array(idx) - print(idx.shape) + # print(idx.shape) idx = idx%(2*T) else: idx = idx % (2 * T) sine = sine_gene(idx, T) - ans = [] - new_his = [] - for i in range(act_gen.shape[0]): - tmp, tmp_his= add_list(act_gen[i], sine[i], history[i]) - ans.append(tmp) - new_his.append(tmp_his) - return np.array(ans).astype(np.float32),np.array(new_his).astype(np.float32) + ans, history = add_list_np(act_gen, sine, history) + ans = ans / 180 * 3.14 + + + return ans.astype(np.float32), history.astype(np.float32) @@ -263,7 +289,7 @@ def run_model(observation, idx, T, save_gen=None): offset = 3 else: offset = 5 - print('offset' ,offset) + # print('offset' ,offset) be_obe =[5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] aft_obs = [23,24,25,26,27,28,17,18,19,20,21,22,11,12,13,14,15,16,5,6,7,8,9,10] be_obe = [i -offset for i in be_obe] @@ -279,16 +305,18 @@ def run_model(observation, idx, T, save_gen=None): # minu = np.array([0, 0, deg_rad(30), 0, deg_rad(-60), 0] * 4) # 6 # observation[:, -24:] -= minu - # act_gen = np.zeros((1,12)) + act_gen = np.zeros((1,12)) if save_gen is not None: save_gen.add_list(act_gen[0]) sine = sine_gene(idx, T) - + # print(sine) ans = [] for act in act_gen: ans.append(add_list(act, sine)) ans = np.array(ans).astype(np.float32) + # print(ans) + # input() # be_ans = ans[:, [0,1,2,3,4,5,6,7,8,9,10,11]] = \ ans[:, [9,10,11,6,7,8,3,4,5,0,1,2]] @@ -340,21 +368,39 @@ def run_model1(observation): # # print("ang_list ", angle_list) # return angle_list -def sine_gen(idx, T): - """ - idx : 0~ 2*T - iter ? - """ - angle_list = np.zeros((idx.shape[0], 12)) - - if __name__=="__main__": # sine_gen(np.zeros(100), 40) - mat = np.zeros((100,12)) - mat = np.where(np.indices(mat)>20, 1, 0) - print(mat) + # idx = [3,2,1,4,1,3,2,3,2,3,1,2,3,2,3,1] + # a = [0,2,3,0] + # b= [ 5, 3, 5,0] + # a= np.array(a) + # b= np.array(b) + # c = np.array([[0.2, 0.5, 0.3, 0.4], [0,0.3,0.2,0]]) + # print(a * (1-c) + b * c) + z = [] + minn = 100 + minnn =[] + for i in range(100): + # x = run_model(np.zeros((1,26)).astype(np.float32), i, 40)[0][0][0] + x = add_list(np.zeros((12)), sine_gene(i, 40)) + if i == 0: + print(x) + if x[1] <= minn: + print(x[1], minn) + minn = x[1] + minnn =x + z.append(x) + z = np.vstack(z) + print(minnn) + x= [i for i in range(z.shape[0])] + import matplotlib.pyplot as plt + plt.plot(x, z) + plt.show() + # mat = np.zeros((100,12)) + # mat = np.where(np.indices(mat)>20, 1, 0) + # print(mat) """ todo 1. on action 的范围 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index 38caead50..3bf55302c 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -73,11 +73,13 @@ namespace raisim { ang_vel_.setZero(); // init_position(); - gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; +double aa = double(30) /180*PI, bb = double(-60) /180*PI; + gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, aa, bb, 0.0, aa, bb, 0.0,aa,bb, 0.0, aa,bb; +// gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; // init_position(gc_init_); init(); - obDim_ = 29; + obDim_ = 26; actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); obDouble_.setZero(obDim_); @@ -107,16 +109,16 @@ namespace raisim { Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(p_gain); - jointPgain.tail(nJoints_)[2] = 300; - jointPgain.tail(nJoints_)[5] = 300; - jointPgain.tail(nJoints_)[8] = 300; - jointPgain.tail(nJoints_)[11] = 300; +// jointPgain.tail(nJoints_)[2] = 300; +// jointPgain.tail(nJoints_)[5] = 300; +// jointPgain.tail(nJoints_)[8] = 300; +// jointPgain.tail(nJoints_)[11] = 300; jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(d_gain); - jointDgain.tail(nJoints_)[2] = 15; - jointDgain.tail(nJoints_)[5] = 15; - jointDgain.tail(nJoints_)[8] = 15; - jointDgain.tail(nJoints_)[11] = 15; +// jointDgain.tail(nJoints_)[2] = 15; +// jointDgain.tail(nJoints_)[5] = 15; +// jointDgain.tail(nJoints_)[8] = 15; +// jointDgain.tail(nJoints_)[11] = 15; anymal_->setPdGains(jointPgain, jointDgain); // std::cout<setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); @@ -219,9 +221,9 @@ namespace raisim { obDouble_ << euler_angle[0], euler_angle[1],// quaternion - ang_vel_[0], - ang_vel_[1], - ang_vel_[2], +// ang_vel_[0], +// ang_vel_[1], +// ang_vel_[2], c_v; // std::cout<<"ang_vel : " << ang_vel_ << std::endl; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 996b3d834..521829ff6 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -4,9 +4,9 @@ record_video: true environment: render: true # just testing commenting - num_envs: 100 + num_envs: 1 eval_every_n: 100 - num_threads: 30 + num_threads: 1 simulation_dt: 0.0025 control_dt: 0.01 max_time: 4 diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 685b47833..9b38cf536 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -7,7 +7,7 @@ import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO from raisimGymTorch.env.deploy.angle_utils import transfer_f ,get_last_position -from raisimGymTorch.env.deploy.onnx_deploy import run_model_with_pt_input_modify, list_pt +from raisimGymTorch.env.deploy.onnx_deploy import run_model_with_pt_input_modify, list_pt, run_model_with_pt_input import os import math import time @@ -225,8 +225,12 @@ # sine = sine_generator(envs_idx, schedule, angle_rate) # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) # action = action.astype(np.float32) + # action1, history_act1 = run_model_with_pt_input(action,envs_idx,schedule,history_act) action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) - + # print(f'action1 {action1}\n action{action}') + # print(f'history1 {history_act1} \n history{history_act}') + # input() + # print(f'3{time.time()}') # todo the transfer has bug reward, dones = env.step(action) # input('1') diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index d009849a3..33517884f 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -196,17 +196,19 @@ # if step%1 ==0 : # input() if step == 0: - draw_vir_obs = Drawer('vir_obs1') + draw_vir_obs = Drawer('vir_obs') draw_vir_action = Drawer('vir_sent_act') draw_vir_recv_action = Drawer('vir_recv_act') waiter.update_start() # save_gen_act = CSV_saver('virtual_gen_act', './') save_obs = CSV_saver('virtual_obs', './') - save_for_work = CSV_saver('virtual_for_work', './') + save_for_work = CSV_saver('virtual_sent', './') + save_recv = CSV_saver('virtual_recv', './') obs = env.observe(False) print('saving') last_p = get_last_position(obs) print(f'last{last_p}') + save_recv.add_list(last_p[0]) draw_vir_recv_action.add_map_list(last_p[0]) if onnx_flag: # obs = rad_deg(obs) @@ -295,6 +297,7 @@ draw_vir_obs.draw() draw_vir_action.draw() draw_vir_recv_action.draw() + save_recv.save() # save_gen_act.save() else: a1.back_safe() From 865d460384591e03e2b82a208313c39713ea5824 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Fri, 18 Aug 2023 15:09:14 +0800 Subject: [PATCH 25/62] now ok to train --- .../raisimGymTorch/algo/ppo/module.py | 5 +- raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py | 7 +- .../env/VectorizedEnvironment.hpp | 4 +- .../raisimGymTorch/env/deploy/onnx_deploy.py | 56 ++++++++++++- .../raisimGymTorch/env/deploy/robot.py | 50 ++--------- .../envs/rsg/Environment_tobuildforpython.hpp | 84 +++++++++---------- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 12 +-- .../raisimGymTorch/env/envs/rsg/runner.py | 45 +++++++--- 8 files changed, 147 insertions(+), 116 deletions(-) diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/module.py b/raisimGymTorch/raisimGymTorch/algo/ppo/module.py index 6d931b4b5..9ec7baf87 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/module.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/module.py @@ -18,8 +18,8 @@ def __init__(self, architecture, distribution, device='cpu'): def sample(self, obs): self.action_mean = self.architecture.architecture(obs).cpu().numpy() actions, log_prob = self.distribution.sample(self.action_mean) - # actions = (actions - actions.min()) / (actions.max() - actions.min()) * np.pi - actions = np.clip(actions, -1, 1) + # todo + # actions = (np.clip(actions, self.action_mean -np.pi, self.action_mean+np.pi) - self.action_mean) / np.pi return actions, log_prob def evaluate(self, obs, actions): @@ -112,6 +112,7 @@ def __init__(self, dim, size, init_std, fast_sampler, seed=0): self.std_np = self.std.detach().cpu().numpy() def update(self): + print(self.std) self.std_np = self.std.detach().cpu().numpy() def sample(self, logits): diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py index 1a1d872d4..df54c47be 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py @@ -76,9 +76,7 @@ def __init__(self, def act(self, actor_obs): self.actor_obs = actor_obs with torch.no_grad(): - # tmp = torch.from_numpy(actor_obs) - self.actions, self.actions_log_prob = self.actor.sample(torch.from_numpy(self.actor_obs).to(self.device)) - + self.actions, self.actions_log_prob = self.actor.sample(torch.from_numpy(actor_obs).to(self.device)) return self.actions def forward(self, actor_obs): @@ -111,6 +109,7 @@ def log(self, variables): def _train_step(self, log_this_iteration): mean_value_loss = 0 mean_surrogate_loss = 0 + # cnnt = 0 for epoch in range(self.num_learning_epochs): for actor_obs_batch, critic_obs_batch, actions_batch, old_sigma_batch, old_mu_batch, current_values_batch, advantages_batch, returns_batch, old_actions_log_prob_batch \ in self.batch_sampler(self.num_mini_batches): @@ -170,5 +169,5 @@ def _train_step(self, log_this_iteration): num_updates = self.num_learning_epochs * self.num_mini_batches mean_value_loss /= num_updates mean_surrogate_loss /= num_updates - + # print('cnnt ', cnnt) return mean_value_loss, mean_surrogate_loss, locals() diff --git a/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp b/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp index 411a6312f..627f468c6 100755 --- a/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp +++ b/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp @@ -8,6 +8,7 @@ #include "RaisimGymEnv.hpp" #include "omp.h" +#include #include "Yaml.hpp" namespace raisim { @@ -188,7 +189,8 @@ class VectorizedEnvironment { if (done[agentId]) { environments_[agentId]->reset(); -// reward[agentId] += terminalReward; + reward[agentId] += terminalReward; +// std::cout<<"agentid "<< agentId <<" reward " << reward[agentId] << std::endl; } } diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index c8f5039cc..ff553a767 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -157,6 +157,56 @@ def sine_gene(idx, T): ans.append(sine_gene(i, T)) return ans +def sine_gene_pt(idx, T): + # print(idx) + if isinstance(idx, np.ndarray) or isinstance(idx, list): + mat = np.zeros((len(idx), 12)) + i_set = set(idx) + idd = {} + for i in i_set: + idd[i] = sine_gene_pt(i, 40) + for i in range(len(idx)): + mat[i] = idd[idx[i]] + return mat + if not isinstance(idx, list): + angle_list = [0 for i in range(12)] + if idx >= 2 * T: + idx = 0 + dh = 0.8 # todo for test + + if idx >= 0 and idx <= T: + tp0 =idx - 0 + y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y2 = 0 + # y2 = y1 + elif idx>T and idx<=2*T: + tp0 =idx - T + y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y1 = 0 + # y1 = y2 + + + angle_list[0] = 0 + angle_list[1] = y1 + angle_list[2] = -2 * y1 + angle_list[3] = 0 + angle_list[4] = y2 + angle_list[5] = -2 * y2 + angle_list[6] = 0 + angle_list[7] = y2 + angle_list[8] = -2 * y2 + angle_list[9] = 0 + angle_list[10] = y1 + angle_list[11] = -2 * y1 + # print("ang_list ", angle_list) + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i]) ) for i in range(12)] + # print("ang_list ", angle_list) + return angle_list + else: + ans = [] + for i in idx: + ans.append(sine_gene_pt(i, T)) + return ans def rate_to_act(lower, upper, rate): @@ -221,10 +271,12 @@ def add_list_np(act_gen, sine, history): kk = 0.9 kf = 1 kb = 0.2 + # print('be history ', history, 'act_gen ', act_gen) history = history*kk + (1-kk) * act_gen ans = np.clip(kb*history + kf * sine, -1, 1) ans = (ans + 1) /2 # 100 * 12 ans = lerp_np(low_np, upp_np, ans) + # print('af history ', history) return ans, history def run_model_with_pt_input(act_gen, idx, T, history): @@ -252,8 +304,8 @@ def run_model_with_pt_input_modify(act_gen, idx, T, history): else: idx = idx % (2 * T) - sine = sine_gene(idx, T) - + sine = sine_gene_pt(idx, T) + # act_gen = np.zeros_like(act_gen) ans, history = add_list_np(act_gen, sine, history) ans = ans / 180 * 3.14 diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/robot.py b/raisimGymTorch/raisimGymTorch/env/deploy/robot.py index a2d066cee..f164332b1 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/robot.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/robot.py @@ -1,44 +1,8 @@ +from math import cos import numpy as np -class Robot(): - def __init__(self): - self.robot = None - self.ob_dims = self.robot.ob_dims if self.robot is not None else 34 - self.act_dims = self.robot.act_dims if self.robot is not None else 12 - pass - - - def connect(self): - """ - connect to A1 with high level first - """ - - pass - - def ob_dim(self) -> int: - return self.ob_dims - - def act_dim(self) -> int: - return self.act_dims - - def alive(self) -> bool: - return True - - def observe(self) -> np.ndarray: - return np.random.rand(1, 34).astype(np.float32) - pass - - def take_action(self, act): - """ - upd recv and send - """ - pass - - def reset(self): - """ - how to implement - maybe to be implement with stand - """ - self.robot.reset() - - - +T=100 +x = [i for i in range(4 * T)] +y = [1.2 * 10 *(-cos(3.14 * 2 * (i % T) /T) + 1) /2 for i in x] +import matplotlib.pyplot as plt +plt.plot(x,y) +plt.show() \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index 3bf55302c..a5fa0d47f 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -79,7 +79,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; // init_position(gc_init_); init(); - obDim_ = 26; + obDim_ = 29; actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); obDouble_.setZero(obDim_); @@ -178,29 +178,31 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; double rrr =0; // for(int i=0;i<=2;i++) rrr += abs(euler_angle[i]) ; rrr = abs(euler_angle[0]) + abs(euler_angle[1]); + rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1]) ); // rrr = abs(gc_[0]) + abs(gc_[1]); // rrr += (gc_.tail(12) - pTarget12_).norm() ; bool accu = false; rewards_.record("Stable",-rrr, accu); +// std::cout<<"eu 0 "<< euler_angle[0] << " eu1 " << euler_angle[1] << std::endl; rewards_.record("Live", 1, accu); // rewards_.record("forwardVel", bodyLinearVel_[0], accu); // rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); - rewards_.record("Wheel", 0.3 - abs(ang_vel_[2]- 0.3), accu); - euler_angle_old = euler_angle; + rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); +// euler_angle_old = euler_angle; // rewards_.record("torque", ) return rewards_.sum(); } void updateObservation() { anymal_->getState(gc_, gv_); - raisim::Vec<4> quat; - raisim::Mat<3,3> rot; - quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; - quat /= quat.norm(); - raisim::quatToRotMat(quat, rot); - bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); - bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// quat /= quat.norm(); +// raisim::quatToRotMat(quat, rot); +// bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); +// bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); anymal_->getAngularVelocity(anymal_->getBodyIdx("base"), ang_vel_); Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); euler_angle = ToEulerAngles(qua); @@ -221,9 +223,9 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; obDouble_ << euler_angle[0], euler_angle[1],// quaternion -// ang_vel_[0], -// ang_vel_[1], -// ang_vel_[2], + ang_vel_[0], + ang_vel_[1], + ang_vel_[2], c_v; // std::cout<<"ang_vel : " << ang_vel_ << std::endl; @@ -236,46 +238,36 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; bool isTerminalState(float& terminalReward) final { terminalReward = float(terminalRewardCoeff_); - -// for(auto& contact: anymal_->getContacts()) -// if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) -// {// if there is any contact body was not in the footIndices the over -// rewards_.record("Live", -10, false); -// return true;} + bool accu = true; + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// rewards_.record("Live", terminalReward, accu); + std::cout<<"foot done " << std::endl; + return true;} if(abs(gc_[2] - gc_init_[2]) > 0.22){ // std::cout<<"z done" << std::endl; - rewards_.record("Live", terminalReward, false); +// rewards_.record("Live", terminalReward, accu); return true; } -// if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.17) -// { -//// std::cout<<"y angle done" << std::endl; -// rewards_.record("Live", terminalReward, false); -// -// return true; -// } -// if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.17) -// { -//// std::cout<<"x angle done" << std::endl; -// rewards_.record("Live", terminalReward, false); + if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.17) + { +// std::cout<<"y angle done" <<" " << euler_angle[1]<< std::endl; +// rewards_.record("Live", terminalReward, accu); + + return true; + } + if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.17) + { +// std::cout<<"x angle done " << euler_angle[0] << std::endl; +// rewards_.record("Live", terminalReward, accu); // -// return true; -// } -// if (abs(gc_[0] - gc_init_[0]) >= 0.4) -// { -// return true; -// } -// if (abs(gc_[1] - gc_init_[1]) >= 0.4) -// { -// return true; -// } -// if(euler_angle[2] < -0.3) -// { -// return true; -// } + return true; + } + terminalReward = 0.f; return false; } @@ -287,7 +279,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; bool visualizable_ = false; raisim::ArticulatedSystem* anymal_, *anymal_1; Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; - double terminalRewardCoeff_ = -100.; + double terminalRewardCoeff_ = -10.; Eigen::VectorXd actionMean_, actionStd_, obDouble_; Vec<3> acc_, ang_vel_; Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 521829ff6..d1c71c6e9 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -4,9 +4,9 @@ record_video: true environment: render: true # just testing commenting - num_envs: 1 - eval_every_n: 100 - num_threads: 1 + num_envs: 100 + eval_every_n: 40 + num_threads: 30 simulation_dt: 0.0025 control_dt: 0.01 max_time: 4 @@ -27,11 +27,11 @@ environment: # torque: # coeff: -4e-5 Stable: - coeff: 0.4 + coeff: 0.3 Live: - coeff: 1 + coeff: 0 Wheel: - coeff: 0.3 + coeff: 0.2 # Mimic: # coeff: 0. diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 9b38cf536..44eee451a 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -99,11 +99,12 @@ num_learning_epochs=4, gamma=0.996, lam=0.95, - num_mini_batches=4, + num_mini_batches=16, + # learning_rate_schedule='', device=device, log_dir=saver.data_dir, shuffle_batch=False, - learning_rate=cfg['environment']['learnning_rate'] + # learning_rate=cfg['environment']['learnning_rate'] ) reward_analyzer = RewardAnalyzer(env, ppo.writer) @@ -124,6 +125,7 @@ mode == 'train' history_act = np.array([his_util] * num_envs) +history_act_0 = np.array([his_util] * num_envs) total_update = args.update if mode =='train' or mode == 'retrain': # print('start train') @@ -135,10 +137,13 @@ start = time.time() env.reset() reward_sum = 0 + envs_idx = [0] * num_envs + history_act=history_act_0 done_sum = 0 average_dones = 0. if update % cfg['environment']['eval_every_n'] == 0: + # draw_his = Drawer('history') waiter = Waiter(0.01) print("Visualizing and evaluating the current policy") torch.save({ @@ -148,15 +153,15 @@ 'optimizer_state_dict': ppo.optimizer.state_dict(), }, saver.data_dir+"/full_"+str(update)+'.pt') # we create another graph just to demonstrate the save/load method - loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) - loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) + # loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) + # loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) # debug_draw_action = Drawer('debug_draw_action') # debug_draw_history = Drawer('debug_draw_history') env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') - envs_idx = [0] * num_envs + # envs_idx = [0] * num_envs waiter.update_start() for step in range(n_steps): with torch.no_grad(): @@ -169,17 +174,24 @@ # history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) - action = loaded_graph.architecture(torch.from_numpy(obs).cpu()) - action = action.cpu().detach().numpy() + action = ppo.act(obs) + # action = action.cpu().detach().numpy() # sine = sine_generator(envs_idx, schedule, angle_rate) # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) # action = action.astype(np.float32) # - +#todo action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + + + + # draw_his.add_map_list(history_act[0]) + # + # history_act = history_act_0 + # print(history_act) # debug_draw_action.add_map_list(action[0]) # debug_draw_history.add_map_list(history_act[0]) @@ -188,8 +200,10 @@ # history_act = sine # envs_idx = list(map(check_done, envs_idx, dones)) # history_act = action - history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) + + # todo + history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) envs_idx = list(map(check_done, envs_idx, dones)) reward_analyzer.add_reward_info(env.get_reward_info()) @@ -197,7 +211,7 @@ wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) # if wait_time > 0.: # time.sleep(wait_time) - + # draw_his.draw() env.stop_video_recording() env.turn_off_visualization() history_act = np.array([his_util] * num_envs) @@ -211,6 +225,7 @@ # actual training # sine = [0] * 12 envs_idx = [0] * num_envs + history_act=history_act_0 for step in range(n_steps): obs = env.observe(False) """ @@ -220,13 +235,18 @@ # history_act = get_last_position(obs) action = ppo.act(obs) - + # if step== 100: + # print(action) # sine = sine_generator(envs_idx, schedule, angle_rate) # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) # action = action.astype(np.float32) # action1, history_act1 = run_model_with_pt_input(action,envs_idx,schedule,history_act) + #todo action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + + # history_act = history_act_0 + # print(f'action1 {action1}\n action{action}') # print(f'history1 {history_act1} \n history{history_act}') # input() @@ -239,6 +259,7 @@ # history_act = sine # envs_idx = list(map(check_done, envs_idx, dones)) # history_act = action + history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) # reward = reward * envs_idx / n_steps * 2 @@ -251,7 +272,7 @@ reward_analyzer.analyze_and_plot(update) # take st step to get value obs obs = env.observe(False) - ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 10 == 0, update=update) + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) average_ll_performance = reward_sum / total_steps if average_ll_performance > biggest_reward: biggest_iter = update From 3b56aa09cdc03731bec30efc534fed731e364736 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Fri, 18 Aug 2023 22:17:25 +0800 Subject: [PATCH 26/62] the skate model has been written wishing to make the model up to the skate --- examples/CMakeLists.txt | 2 + examples/src/server/go1.cpp | 72 +++++++++++-------- examples/src/server/skate.cpp | 63 ++++++++++++++++ .../raisimGymTorch/algo/ppo/module.py | 3 +- .../raisimGymTorch/env/deploy/onnx_deploy.py | 13 ++-- .../envs/rsg/Environment_tobuildforpython.hpp | 32 +++++---- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 10 +-- .../raisimGymTorch/env/envs/rsg/runner.py | 31 ++++---- 8 files changed, 160 insertions(+), 66 deletions(-) create mode 100644 examples/src/server/skate.cpp diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 24d95b4bb..2af672918 100755 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -98,3 +98,5 @@ create_executable(anymals src/maps/anymals.cpp) create_executable(hill1 src/maps/hill1.cpp) create_executable(kinova src/maps/kinova.cpp) +create_executable(skate src/server/skate.cpp) + diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 09abd91a8..32abb187d 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -140,12 +140,15 @@ int main(int argc, char* argv[]) { /// create objects auto ground = world.addGround(); auto go1 = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\a1_description\\urdf\\a1.urdf"); - + auto board = world.addArticulatedSystem(binaryPath.getDirectory() +"\\rsc\\skate\\skate.urdf"); /// go1 joint PD controller Eigen::VectorXd jointNominalConfig(go1->getGeneralizedCoordinateDim()), jointVelocityTarget(go1->getDOF()); // jointNominalConfig << 0, 0, 0.41, 1.0, 0.0, 0.0, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; - - jointNominalConfig << 0, 0, 0.33, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list + Eigen::VectorXd board_posi(board->getGeneralizedCoordinateDim()); + board_posi << 0,0,0.11, 0.707, 0,0, 0.707,0,0; + board->setGeneralizedCoordinate(board_posi); + board->setName("board"); + jointNominalConfig << -0, 0, 0.49, 1.0, 0.0, 0, 0, 0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976, 0.0, 0.5235987755982988, -1.0471975511965976 ; // todo implement in python using list // jointNominalConfig << 0, 0, 0.33, 1.0, 0.0, 0, 0, 0, 1, -2.2, 0.0, 1, -2.2, 0.0, 1, -2.2, 0.0, 1, -2.2; // todo implement in python using list // jointNominalConfig.tail(12).setZero(); // Eigen::VectorXd tmp(12); @@ -190,7 +193,7 @@ int main(int argc, char* argv[]) { for (int i=0; i<2000000; i++) { - std::cin.ignore(); +// std::cin.ignore(); // imu->getAngularVelocity() @@ -208,22 +211,22 @@ int main(int argc, char* argv[]) { bodyAngularVelocity = rot.e().transpose() * gv.segment(3, 3); Eigen::Vector3d eule; rot2euler(rot, eule); - std::cout<<"euler " << bodyAngularVelocity< qu{1+double(i)/500 , 0 , 0, 0+ double(i) / 500 }; // go1->setBaseOrientation(qu); - Eigen::VectorXd tmp1(12), tmp2(12); - tmp1 = gc.tail(12); - tmp2 = gv.tail(12); - for(auto i=0; i<12; i++){ - ob_[2*i] = tmp1[i]; - ob_[2 * i+1] = tmp2[i]; - } - for(auto i=0;i <12; i++) - { - swap(ob_, i, 12 +i); - } +// Eigen::VectorXd tmp1(12), tmp2(12); +// tmp1 = gc.tail(12); +// tmp2 = gv.tail(12); +// for(auto i=0; i<12; i++){ +// ob_[2*i] = tmp1[i]; +// ob_[2 * i+1] = tmp2[i]; +// } +// for(auto i=0;i <12; i++) +// { +// swap(ob_, i, 12 +i); +// } raisim::Vec<3> vel_temp; // go1->getFrameVelocity("ROOT", vel_temp); @@ -234,13 +237,19 @@ int main(int argc, char* argv[]) { // acc[i] = vel_temp // } // std::cout << "vel \n" << vel << std::endl << "acc\n " << acc << std::endl; - usleep(5000); + usleep(10000); angle_generator(position, i, 40, 0.35); jointNominalConfig.tail(12) = position; - for(auto n: go1->getBodyNames()) - { - std::cout << n << std::endl; - } +// for(auto n: go1->getContacts()) +// { +// std::cout << n.getPairContactIndexInPairObject()<< std::endl; +// } + for(auto obj:world.getObjList()) + { + std::cout<getName()< angvel; go1->getAngularVelocity(roott, angvel); // std::cout<printOutFrameNamesInOrder(); +// std::cout<< "angle" << b/odyAngularVelocity << std::endl; +// std::cout<<"GET " << angvel << std::endl; +// go1->printOutFrameNamesInOrder(); go1->getFrameAcceleration("floating_base", angvel); - std::cout<getAngularVelocity(); RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) @@ -275,10 +284,17 @@ int main(int argc, char* argv[]) { // } // jointNominalConfig.tail(12) = tmp; // std::cout<< tmp << std::endl; - +// jointNominalConfig[8] = - 0.01 * i ; go1->setPdTarget(jointNominalConfig, jointVelocityTarget); - +// for(auto& contact : go1->getContacts()) +// { +//// if(contact.getPairContactIndexInPairObject() != raisim::BodyType::STATIC) +//// { +//// world.getObject(contact.getPairObjectIndex())->getContacts(); +//// } +// } +// server.integrateWorldThreadSafe(); } diff --git a/examples/src/server/skate.cpp b/examples/src/server/skate.cpp new file mode 100644 index 000000000..78dfcc449 --- /dev/null +++ b/examples/src/server/skate.cpp @@ -0,0 +1,63 @@ +// +// Created by lr-2002 on 23-8-18. +// +// This file is part of RaiSim. You must obtain a valid license from RaiSim Tech +// Inc. prior to usage. + +#include "raisim/RaisimServer.hpp" +#include "raisim/World.hpp" +#include + +int main(int argc, char* argv[]) { + auto binaryPath = raisim::Path::setFromArgv(argv[0]); + + /// create raisim world + raisim::World world; + world.setTimeStep(0.01); + + /// create objects + world.addGround(); + raisim::RaisimServer server(&world); + server.launchServer(); + auto skate = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\skate\\skate.urdf"); + /// skate state + Eigen::VectorXd jointNominalConfig(skate->getGeneralizedCoordinateDim()); + skate->setName("skate"); + jointNominalConfig.setZero(); +// jointNominalConfig[1] = 0.01; + jointNominalConfig<< 0, 0, 0.11, 1 , 0, 0, 0 ,0 , 0; + Eigen::VectorXd jointVelocityTarget(skate->getDOF() ); + jointVelocityTarget.setZero(); + skate->setControlMode(raisim::ControlMode::FORCE_AND_TORQUE); + Eigen::VectorXd jointPgain(skate->getDOF()), jointDgain(skate->getDOF()); + jointPgain.tail(2).setConstant(80); + jointDgain.tail(2).setConstant(4); + + skate->setGeneralizedCoordinate(jointNominalConfig); + skate->setGeneralizedForce(Eigen::VectorXd::Zero(skate->getDOF())); + skate->setPdGains(jointPgain, jointDgain); + skate->setPdTarget(jointNominalConfig, jointVelocityTarget); + skate->setGeneralizedCoordinate(jointNominalConfig); + std::cout << " position " << jointNominalConfig; + /// launch raisim server + + std::cout<<"gra " << world.getGravity(); + std::cout << "\n mov "; + auto na = skate->getMovableJointNames(); + + server.focusOn(skate); + for (auto i:na) + { + std::cout << i << std::endl; + } + for (int i=0; i<200000; i++) { + RS_TIMED_LOOP(int(world.getTimeStep()*1e6)) + server.integrateWorldThreadSafe(); + jointNominalConfig.tail(2).setConstant( 0.02 * i); + jointVelocityTarget.tail(2).setConstant(0); + skate->setPdTarget(jointNominalConfig, jointVelocityTarget); + } + + server.killServer(); +} + diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/module.py b/raisimGymTorch/raisimGymTorch/algo/ppo/module.py index 9ec7baf87..b888a9b36 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/module.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/module.py @@ -19,7 +19,8 @@ def sample(self, obs): self.action_mean = self.architecture.architecture(obs).cpu().numpy() actions, log_prob = self.distribution.sample(self.action_mean) # todo - # actions = (np.clip(actions, self.action_mean -np.pi, self.action_mean+np.pi) - self.action_mean) / np.pi + # actions = actions- + # actions =np.clip(actions- 1,-1 ,1) return actions, log_prob def evaluate(self, obs, actions): diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index ff553a767..c3514b444 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -15,8 +15,11 @@ def ang_trans(lower, upper, x): """ trans the rate x to the angle """ - x = (x+1)/2 #todo for test - return x * upper + (1-x) * lower + if isinstance(lower, list): + return [ang_trans(lower[i], upper[i], x[i]) for i in range(len(lower))] + else: + x = (x+1)/2 #todo for test + return x * upper + (1-x) * lower def norm(lower, upper, x): # print(lower, upper, x) @@ -58,6 +61,7 @@ def deg_normalize(lower, upper, x): # low_rad = deg_rad(low) # upp_rad = deg_rad(upp) u0 = deg_normalize(low, upp, u0) +u0_ang = ang_trans(low, upp, u0) # u0_rad = rad_normalize(low_rad, upp_rad, u0_rad) # u0 = [-deg_normalize(low, upp, u0)[i] if deg_normalize(low, upp, u0)[i]!=0 else 0 for i in range(12)] # print(u0) @@ -172,7 +176,8 @@ def sine_gene_pt(idx, T): angle_list = [0 for i in range(12)] if idx >= 2 * T: idx = 0 - dh = 0.8 # todo for test + dh =0.8 #:w + if idx >= 0 and idx <= T: tp0 =idx - 0 @@ -199,7 +204,7 @@ def sine_gene_pt(idx, T): angle_list[10] = y1 angle_list[11] = -2 * y1 # print("ang_list ", angle_list) - angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i]) ) for i in range(12)] + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + u0_ang[i]) for i in range(12)] # print("ang_list ", angle_list) return angle_list else: diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index a5fa0d47f..f15214b44 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -144,7 +144,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; anymal_->setState(gc_init_, gv_init_); rewards_.setZero(); COUNT=0; - euler_angle_old.setZero(); +// euler_angle_old.setZero(); updateObservation(); } @@ -177,33 +177,35 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; updateObservation(); double rrr =0; // for(int i=0;i<=2;i++) rrr += abs(euler_angle[i]) ; - rrr = abs(euler_angle[0]) + abs(euler_angle[1]); - rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1]) ); + rrr = abs(euler_angle[0]) + abs(euler_angle[1]) + abs(euler_angle[2]); + rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1]) +abs(ang_vel_[2])); // rrr = abs(gc_[0]) + abs(gc_[1]); // rrr += (gc_.tail(12) - pTarget12_).norm() ; bool accu = false; rewards_.record("Stable",-rrr, accu); // std::cout<<"eu 0 "<< euler_angle[0] << " eu1 " << euler_angle[1] << std::endl; rewards_.record("Live", 1, accu); -// rewards_.record("forwardVel", bodyLinearVel_[0], accu); + rewards_.record("forwardVel", bodyLinearVel_[0], accu); // rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); - rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); +// rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); // euler_angle_old = euler_angle; -// rewards_.record("torque", ) + rewards_.record("torque",anymal_->getGeneralizedForce().squaredNorm() ); return rewards_.sum(); } void updateObservation() { anymal_->getState(gc_, gv_); -// raisim::Vec<4> quat; -// raisim::Mat<3,3> rot; -// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; -// quat /= quat.norm(); -// raisim::quatToRotMat(quat, rot); -// bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); -// bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + quat /= quat.norm(); + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); anymal_->getAngularVelocity(anymal_->getBodyIdx("base"), ang_vel_); +// anymal_->getVelocity(anymal_->getBodyIdx("base"), bodyLinearVel_); +// anymal_->get Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); euler_angle = ToEulerAngles(qua); // if(euler_angle[1] > PI) euler_angle[1] -= 2*PI; @@ -243,9 +245,9 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) {// if there is any contact body was not in the footIndices the over // rewards_.record("Live", terminalReward, accu); - std::cout<<"foot done " << std::endl; +// std::cout<<"foot done " << std::endl; return true;} - if(abs(gc_[2] - gc_init_[2]) > 0.22){ + if(abs(gc_[2] - gc_init_[2]) > 0.2){ // std::cout<<"z done" << std::endl; // rewards_.record("Live", terminalReward, accu); diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index d1c71c6e9..927305ac2 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -22,16 +22,16 @@ environment: d_gain: 8 urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: -# forwardVel: -# coeff: 0 -# torque: -# coeff: -4e-5 + forwardVel: + coeff: 0.2 + torque: + coeff: -4e-5 Stable: coeff: 0.3 Live: coeff: 0 Wheel: - coeff: 0.2 + coeff: 0. # Mimic: # coeff: 0. diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 44eee451a..e986795bf 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -122,7 +122,7 @@ # weight_path = "" print = logger.info - +env_idx = np.zeros(100) mode == 'train' history_act = np.array([his_util] * num_envs) history_act_0 = np.array([his_util] * num_envs) @@ -137,7 +137,7 @@ start = time.time() env.reset() reward_sum = 0 - envs_idx = [0] * num_envs + envs_idx = env_idx history_act=history_act_0 done_sum = 0 average_dones = 0. @@ -161,7 +161,7 @@ env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') - # envs_idx = [0] * num_envs + envs_idx = env_idx waiter.update_start() for step in range(n_steps): with torch.no_grad(): @@ -175,6 +175,8 @@ action = ppo.act(obs) + aa = action.copy() + aa = np.clip(aa-1, -1, 1) # todo # action = action.cpu().detach().numpy() # sine = sine_generator(envs_idx, schedule, angle_rate) @@ -182,7 +184,7 @@ # action = action.astype(np.float32) # #todo - action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + aa, history_act = run_model_with_pt_input_modify(aa, envs_idx, schedule, history_act) @@ -195,7 +197,7 @@ # debug_draw_action.add_map_list(action[0]) # debug_draw_history.add_map_list(history_act[0]) - reward, dones = env.step(action) + reward, dones = env.step(aa) # history_act = sine # envs_idx = list(map(check_done, envs_idx, dones)) @@ -204,8 +206,8 @@ # todo history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) - envs_idx = list(map(check_done, envs_idx, dones)) - + # envs_idx = list(map(check_done, envs_idx, dones)) + envs_idx = np.where(dones ==1, 0, envs_idx+1) reward_analyzer.add_reward_info(env.get_reward_info()) frame_end = time.time() wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) @@ -224,7 +226,7 @@ # actual training # sine = [0] * 12 - envs_idx = [0] * num_envs + envs_idx = env_idx history_act=history_act_0 for step in range(n_steps): obs = env.observe(False) @@ -235,6 +237,8 @@ # history_act = get_last_position(obs) action = ppo.act(obs) + aa = action.copy() + aa = np.clip(aa - 1, -1, 1) # todo # if step== 100: # print(action) @@ -242,8 +246,8 @@ # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) # action = action.astype(np.float32) # action1, history_act1 = run_model_with_pt_input(action,envs_idx,schedule,history_act) - #todo - action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + + aa, history_act = run_model_with_pt_input_modify(aa, envs_idx, schedule, history_act) # history_act = history_act_0 @@ -251,8 +255,8 @@ # print(f'history1 {history_act1} \n history{history_act}') # input() # print(f'3{time.time()}') - # todo the transfer has bug - reward, dones = env.step(action) + + reward, dones = env.step(aa) # input('1') # print(f"history : {history_act}") @@ -263,8 +267,9 @@ history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) # reward = reward * envs_idx / n_steps * 2 + envs_idx = np.where(dones == 1, 0, envs_idx + 1) - envs_idx = list(map(check_done, envs_idx, dones)) + # envs_idx = list(map(check_done, envs_idx, dones)) ppo.step(value_obs=obs, rews=reward, dones=dones) done_sum = done_sum + np.sum(dones) reward_sum = reward_sum + np.sum(reward) From fdd34ac2cf819ff54dc4f2e955fdd131d88ca32c Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Fri, 18 Aug 2023 23:33:20 +0800 Subject: [PATCH 27/62] onnx_deploy.py sine_pt need fix the boundry you can let the angle lower --- .../raisimGymTorch/env/deploy/onnx_deploy.py | 43 ++++++++++--------- .../envs/rsg/Environment_tobuildforpython.hpp | 40 ++++++++++------- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 6 ++- .../raisimGymTorch/env/envs/rsg/runner.py | 2 +- 4 files changed, 53 insertions(+), 38 deletions(-) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index c3514b444..8126e8fe1 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -23,7 +23,7 @@ def ang_trans(lower, upper, x): def norm(lower, upper, x): # print(lower, upper, x) - assert abs((x - lower) / (upper - lower) ) <= 1 + assert abs((x - lower) / (upper - lower) ) <= 1, f"{abs((x - lower) / (upper - lower) )} {x, upper, lower} " return (x - lower) / (upper - lower) def rad_normalize(lower, upper,x): @@ -53,7 +53,9 @@ def deg_normalize(lower, upper, x): low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] -u0 = [0, 30, -60,0, 30, -60,0, 30, -60,0, 30, -60] +tha1,tha2 = 60, 30 +u0 = [0, tha2, -2*tha2, 0 ,tha1, -tha1 * 2, 0 , tha2, -2*tha2, 0, tha1,-2*tha1] + low_np = np.array(low) upp_np = np.array(upp) # u0_rad = deg_rad(u0) @@ -176,21 +178,22 @@ def sine_gene_pt(idx, T): angle_list = [0 for i in range(12)] if idx >= 2 * T: idx = 0 - dh =0.8 #:w - - - if idx >= 0 and idx <= T: - tp0 =idx - 0 - y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - y2 = 0 - # y2 = y1 - elif idx>T and idx<=2*T: - tp0 =idx - T - y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - y1 = 0 + dh =-0.5 #:w + + + # if idx >= 0 and idx <= T: + # tp0 =idx - 0 + # y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + # y2 = 0 + # # y2 = y1 + # elif idx>T and idx<=2*T: + # tp0 =idx - T + # y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + # y1 = 0 # y1 = y2 - - + idx = idx % T + y1 = dh * 10 * (-cos(pi * 2 *idx /T) + 1) + y2 = deg_rad(60) angle_list[0] = 0 angle_list[1] = y1 angle_list[2] = -2 * y1 @@ -198,11 +201,11 @@ def sine_gene_pt(idx, T): angle_list[4] = y2 angle_list[5] = -2 * y2 angle_list[6] = 0 - angle_list[7] = y2 - angle_list[8] = -2 * y2 + angle_list[7] = y1 + angle_list[8] = -2 * y1 angle_list[9] = 0 - angle_list[10] = y1 - angle_list[11] = -2 * y1 + angle_list[10] = y2 + angle_list[11] = -2 * y2 # print("ang_list ", angle_list) angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + u0_ang[i]) for i in range(12)] # print("ang_list ", angle_list) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index f15214b44..6c0643880 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -54,6 +54,7 @@ namespace raisim { READ_YAML(double, p_gain, cfg_["p_gain"]); READ_YAML(double, d_gain, cfg_["d_gain"]); anymal_ = world_->addArticulatedSystem(urdf_path); + skate = world_ ->addArticulatedSystem("/home/lr-2002/code/raisimLib/rsc/skate/skate.urdf"); anymal_->setName("model"); anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); world_->addGround(); @@ -62,7 +63,10 @@ namespace raisim { gcDim_ = anymal_->getGeneralizedCoordinateDim(); // 19 gvDim_ = anymal_->getDOF(); // 18 nJoints_ = gvDim_ - 6; - + skate_posi_init.setZero(9); + skate_vel_init.setZero(8); + skate_posi_init << 0, 0.12, 0.11, 0.707, 0., 0, 0.707, 0 , 0; + skate ->setGeneralizedCoordinate(skate_posi_init); gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); euler_angle.setZero();euler_angle_old.setZero(); @@ -71,10 +75,12 @@ namespace raisim { gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); acc_.setZero();//1 ang_vel_.setZero(); - + skate_vel_.setZero(8); + skate_posi_.setZero(9); // init_position(); -double aa = double(30) /180*PI, bb = double(-60) /180*PI; - gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, aa, bb, 0.0, aa, bb, 0.0,aa,bb, 0.0, aa,bb; +double aa = double(30) /180*PI, bb = double(60) /180*PI; + gc_init_<< 0, 0, 0.35, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa,-2*aa, 0.0, bb, -2*bb; + // gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; // init_position(gc_init_); init(); @@ -142,6 +148,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; void reset() final { anymal_->setState(gc_init_, gv_init_); + skate->setState(skate_posi_init, skate_vel_init); rewards_.setZero(); COUNT=0; // euler_angle_old.setZero(); @@ -185,7 +192,8 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; rewards_.record("Stable",-rrr, accu); // std::cout<<"eu 0 "<< euler_angle[0] << " eu1 " << euler_angle[1] << std::endl; rewards_.record("Live", 1, accu); - rewards_.record("forwardVel", bodyLinearVel_[0], accu); + rewards_.record("forwardVel", skate_vel_[0], accu); + rewards_.record("height", 0.45- abs(gc_[2] - 0.45) - abs(gc_[0] ) - abs(gc_[1]) , accu); // rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); // rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); @@ -196,6 +204,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; void updateObservation() { anymal_->getState(gc_, gv_); + skate -> getState(skate_posi_, skate_vel_); raisim::Vec<4> quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; @@ -241,13 +250,13 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; bool isTerminalState(float& terminalReward) final { terminalReward = float(terminalRewardCoeff_); bool accu = true; - for(auto& contact: anymal_->getContacts()) - if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) - {// if there is any contact body was not in the footIndices the over -// rewards_.record("Live", terminalReward, accu); -// std::cout<<"foot done " << std::endl; - return true;} - if(abs(gc_[2] - gc_init_[2]) > 0.2){ +// for(auto& contact: anymal_->getContacts()) +// if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) +// {// if there is any contact body was not in the footIndices the over +//// rewards_.record("Live", terminalReward, accu); +//// std::cout<<"foot done " << std::endl; +// return true;} + if(gc_[2] - gc_init_[2] > 0.3){ // std::cout<<"z done" << std::endl; // rewards_.record("Live", terminalReward, accu); @@ -255,14 +264,14 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; } - if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.17) + if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.3) { // std::cout<<"y angle done" <<" " << euler_angle[1]<< std::endl; // rewards_.record("Live", terminalReward, accu); return true; } - if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.17) + if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.3) { // std::cout<<"x angle done " << euler_angle[0] << std::endl; // rewards_.record("Live", terminalReward, accu); @@ -279,7 +288,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; private: int gcDim_, gvDim_, nJoints_; bool visualizable_ = false; - raisim::ArticulatedSystem* anymal_, *anymal_1; + raisim::ArticulatedSystem* anymal_, *anymal_1, *skate; Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; double terminalRewardCoeff_ = -10.; Eigen::VectorXd actionMean_, actionStd_, obDouble_; @@ -298,6 +307,7 @@ double aa = double(30) /180*PI, bb = double(-60) /180*PI; Eigen::Vector3d euler_angle, euler_angle_old; /// these variables are not in use. They are placed to show you how to create a random number sampler. bool float_base; + Eigen::VectorXd skate_vel_, skate_posi_,skate_posi_init, skate_vel_init; std::normal_distribution normDist_; thread_local static std::mt19937 gen_; }; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 927305ac2..e0d4d6c53 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -23,11 +23,13 @@ environment: urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: forwardVel: - coeff: 0.2 + coeff: 0.5 torque: coeff: -4e-5 Stable: - coeff: 0.3 + coeff: 0.01 + height: + coeff: 0. Live: coeff: 0 Wheel: diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index e986795bf..e373474c7 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -172,7 +172,7 @@ # history_act = get_last_position(obs) # history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) - + # time.sleep(1) action = ppo.act(obs) aa = action.copy() From cf8919c9fa6bd8bd4a7454ef05e49ee06af11c55 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Sun, 20 Aug 2023 20:40:19 +0800 Subject: [PATCH 28/62] update running file --- examples/src/server/go1.cpp | 52 ++++++- raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py | 3 +- .../raisimGymTorch/env/deploy/deploy.py | 141 ----------------- .../raisimGymTorch/env/deploy/onnx_deploy.py | 113 +------------- .../raisimGymTorch/env/deploy/robot.py | 8 - .../raisimGymTorch/env/envs/rsg/runner.py | 144 ++---------------- .../env/envs/rsg/sim_with_real.py | 16 -- .../helper/raisim_gym_helper.py | 8 +- raisimUnity/linux/.gitignore | 0 9 files changed, 75 insertions(+), 410 deletions(-) delete mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/deploy.py delete mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/robot.py mode change 100644 => 100755 raisimUnity/linux/.gitignore diff --git a/examples/src/server/go1.cpp b/examples/src/server/go1.cpp index 32abb187d..b19442107 100644 --- a/examples/src/server/go1.cpp +++ b/examples/src/server/go1.cpp @@ -138,7 +138,7 @@ int main(int argc, char* argv[]) { world.setTimeStep(0.01); /// create objects - auto ground = world.addGround(); + auto ground = world.addGround(0, "land"); auto go1 = world.addArticulatedSystem(binaryPath.getDirectory() + "\\rsc\\a1_description\\urdf\\a1.urdf"); auto board = world.addArticulatedSystem(binaryPath.getDirectory() +"\\rsc\\skate\\skate.urdf"); /// go1 joint PD controller @@ -167,6 +167,35 @@ int main(int argc, char* argv[]) { go1->setPdTarget(jointNominalConfig, jointVelocityTarget); go1->setName("go1"); std::cout<< go1->getGeneralizedCoordinate(); + + for(auto n : go1->getCollisionBodies()) + { + auto name = n.colObj->name; + go1->getCollisionBody(name).setMaterial("steel"); +// go1->getCollisionBody() + } + + for(auto n : board->getCollisionBodies()) + { + auto name = n.colObj->name; + std::cout << name << std::endl; +// board->getCollisionBody(name).setMaterial("steel"); +// go1->getCollisionBody() + } +// wrl + board->getCollisionBody("base/0").setMaterial("sandpaper"); + board->getCollisionBody("rotater_r/0").setMaterial("rubber"); + board->getCollisionBody("rotater_f/0").setMaterial("rubber"); + go1->getCollisionBody("FL_foot/0").setMaterial("rubber"); + go1->getCollisionBody("FR_foot/0").setMaterial("rubber"); + go1->getCollisionBody("RR_foot/0").setMaterial("rubber"); + go1->getCollisionBody("RL_foot/0").setMaterial("rubber"); + world.setMaterialPairProp("steel", "rubber", 0.8, 0.15, 0.001); + world.setMaterialPairProp("rubber", "sandpaper", 0.99, 0.15, 0.001); + world.setMaterialPairProp("land", "rubber", 0.8, 0.1,0.001); + world.setMaterialPairProp("sandpaper", "land", 0.4, 0.15,0.001); + world.setMaterialPairProp("steel","land", 0.1, 0.05,0.001); +// world.updateMaterialProp(); /// launch raisim server raisim::RaisimServer server(&world); server.focusOn(go1); @@ -179,6 +208,8 @@ int main(int argc, char* argv[]) { position.setZero(12), velo.setZero(12); gc.setZero(19), gv.setZero(18); size_t dof = go1->getDOF(); +// go1->set + raisim::Vec<3> vel; vel.setZero(); @@ -189,6 +220,7 @@ int main(int argc, char* argv[]) { bodyAngularVelocity.setZero(3); bodyLinearVelocity.setZero(3); auto roott = go1->getBodyIdx("base"); +// go1->getCollisionBodies(); // auto imu = go1->getSensor("imu_joint"); for (int i=0; i<2000000; i++) { @@ -206,7 +238,15 @@ int main(int argc, char* argv[]) { quat[0] = gc[3]; quat[1] = gc[4]; quat[2] = gc[5]; quat[3] = gc[6]; quat /= quat.norm(); raisim::quatToRotMat(quat, rot); - +// auto nn =go1->getCollisionBodies(); +// int cnt = 0; +// for(auto n : nn ) +// { +// +// std::cout<< cnt ++ << " " << n.getMaterial() << std::endl; +// } +// std::cout<getCollisionBody("FL_FOOT/0").getMaterial(); bodyLinearVelocity = rot.e().transpose() * gv.segment(0, 3); bodyAngularVelocity = rot.e().transpose() * gv.segment(3, 3); Eigen::Vector3d eule; @@ -244,10 +284,10 @@ int main(int argc, char* argv[]) { // { // std::cout << n.getPairContactIndexInPairObject()<< std::endl; // } - for(auto obj:world.getObjList()) - { - std::cout<getName()<getName()< np.array: - """ - cal the act to the limit and add it with the sine - - """ - sine = np.array(sine) - act = cal_limit(act) - return act + sine - -def run(): - """ - run the ob-act - """ - cnt = 0 - angle_list = [0 for x in range(12)] - while True: - print('running func') - if not robot.alive(): - break - - ob = robot.observe() - - act = actor.architecture(torch.from_numpy(ob).cpu()).cpu().detach().numpy() - - angle_list = sine_generator(angle_list, cnt, cfg['environment']['schedule'], cfg['environment']['angle_rate']) - - act = act_concate(act, angle_list) - - robot.take_action(act) - - time.sleep(0.1) - - cnt += 1 - -if __name__=='__main__': - init() - - run() - diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py index 8126e8fe1..785f9059f 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py @@ -23,7 +23,7 @@ def ang_trans(lower, upper, x): def norm(lower, upper, x): # print(lower, upper, x) - assert abs((x - lower) / (upper - lower) ) <= 1, f"{abs((x - lower) / (upper - lower) )} {x, upper, lower} " + # assert abs((x - lower) / (upper - lower) ) <= 1, f"{abs((x - lower) / (upper - lower) )} {x, upper, lower} " return (x - lower) / (upper - lower) def rad_normalize(lower, upper,x): @@ -178,7 +178,7 @@ def sine_gene_pt(idx, T): angle_list = [0 for i in range(12)] if idx >= 2 * T: idx = 0 - dh =-0.5 #:w + dh =-0.5#:w # if idx >= 0 and idx <= T: @@ -206,9 +206,9 @@ def sine_gene_pt(idx, T): angle_list[9] = 0 angle_list[10] = y2 angle_list[11] = -2 * y2 - # print("ang_list ", angle_list) + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + u0_ang[i]) for i in range(12)] - # print("ang_list ", angle_list) + return angle_list else: ans = [] @@ -228,8 +228,6 @@ def rate_to_act(lower, upper, rate): return lerp(lower, upper, norm_for_act(rate)) def clip(a,b,c): - # print('before clip ', a, b, c) - if isinstance(a, np.ndarray): return np.clip(a,b,c) @@ -237,15 +235,12 @@ def clip(a,b,c): a = b if a >= c: a = c - # print('clip ', a) return a def add_list(act_gen, sine, history=None): if isinstance(act_gen, np.ndarray): - # print(act_gen) assert act_gen.shape[0] == 12 else: - # print(act_gen) assert len(act_gen) == 12 kk = 0.9 kf = 1 @@ -259,17 +254,11 @@ def add_list(act_gen, sine, history=None): ans = [rate_to_act(low[i], upp[i], clip(kb * history_u[i] + kf * sine[i], -1, 1)) for i in range(12)] ans = [deg_rad(x) for x in ans] - # todo swap the ans - # print(ans) - # input()/ return ans else: - # history_u = history history = [history[i] * kk + (1-kk) * act_gen[i] for i in range(12)] # todo the act_gen[i] is to big ans = [rate_to_act(low[i], upp[i], clip(kb * history[i] + kf * sine[i], -1, 1)) for i in range(12)] ans = [deg_rad(x) for x in ans] - # todo swap the ans - return ans, history def lerp_np(a, b, c): @@ -279,45 +268,26 @@ def add_list_np(act_gen, sine, history): kk = 0.9 kf = 1 kb = 0.2 - # print('be history ', history, 'act_gen ', act_gen) history = history*kk + (1-kk) * act_gen ans = np.clip(kb*history + kf * sine, -1, 1) ans = (ans + 1) /2 # 100 * 12 ans = lerp_np(low_np, upp_np, ans) - # print('af history ', history) - return ans, history -def run_model_with_pt_input(act_gen, idx, T, history): - if isinstance(idx, list): - idx = [i%(2*T) for i in idx] - else: - idx = idx % (2 * T) - # act_gen = act_gen/3.14 * 180 - # act_gen = np.zeros_like(act_gen) - sine = sine_gene(idx, T) - ans = [] - new_his = [] - for i in range(act_gen.shape[0]): - tmp, tmp_his= add_list(act_gen[i], sine[i], history[i]) - ans.append(tmp) - new_his.append(tmp_his) + return ans, history - return np.array(ans).astype(np.float32),np.array(new_his).astype(np.float32) def run_model_with_pt_input_modify(act_gen, idx, T, history): + act_gen = np.clip(act_gen- 1, -1, 1) if isinstance(idx, list): idx = np.array(idx) - # print(idx.shape) idx = idx%(2*T) else: idx = idx % (2 * T) sine = sine_gene_pt(idx, T) - # act_gen = np.zeros_like(act_gen) ans, history = add_list_np(act_gen, sine, history) ans = ans / 180 * 3.14 - return ans.astype(np.float32), history.astype(np.float32) @@ -370,14 +340,10 @@ def run_model(observation, idx, T, save_gen=None): save_gen.add_list(act_gen[0]) sine = sine_gene(idx, T) - # print(sine) ans = [] for act in act_gen: ans.append(add_list(act, sine)) ans = np.array(ans).astype(np.float32) - # print(ans) - # input() - # be_ans = ans[:, [0,1,2,3,4,5,6,7,8,9,10,11]] = \ ans[:, [9,10,11,6,7,8,3,4,5,0,1,2]] ans = [ans] @@ -388,57 +354,8 @@ def run_model(observation, idx, T, save_gen=None): def run_model1(observation): return model.run([output_name], input_feed={input_name: observation}) -# 运行模型 - -# outputs = model.run([output_name], input_feed={input_name: input_data}) -# print(outputs) -# x = run_model(input_data) -# print(x) - - - # if not isinstance(idx, list): - # angle_list = [0 for i in range(12)] - # if idx >= 2 * T: - # idx = 0 - # dh = 0.8 # todo for test - # if idx >= 0 and idx <= T: - # tp0 =idx - 0 - # y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - # y2 = 0 - # elif idx>T and idx<=2*T: - # tp0 =idx - T - # y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 - # y1 = 0 - # - # - # angle_list[0] = 0 - # angle_list[1] = y1 - # angle_list[2] = -2 * y1 - # angle_list[3] = 0 - # angle_list[4] = y2 - # angle_list[5] = -2 * y2 - # angle_list[6] = 0 - # angle_list[7] = y2 - # angle_list[8] = -2 * y2 - # angle_list[9] = 0 - # angle_list[10] = y1 - # angle_list[11] = -2 * y1 - # # print("ang_list ", angle_list) - # angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + ang_trans(low[i], upp[i], u0[i]) ) for i in range(12)] - # # print("ang_list ", angle_list) - # return angle_list - - - if __name__=="__main__": - # sine_gen(np.zeros(100), 40) - # idx = [3,2,1,4,1,3,2,3,2,3,1,2,3,2,3,1] - # a = [0,2,3,0] - # b= [ 5, 3, 5,0] - # a= np.array(a) - # b= np.array(b) - # c = np.array([[0.2, 0.5, 0.3, 0.4], [0,0.3,0.2,0]]) - # print(a * (1-c) + b * c) + z = [] minn = 100 minnn =[] @@ -458,23 +375,7 @@ def run_model1(observation): import matplotlib.pyplot as plt plt.plot(x, z) plt.show() - # mat = np.zeros((100,12)) - # mat = np.where(np.indices(mat)>20, 1, 0) - # print(mat) -""" -todo -1. on action 的范围 -2. 似乎是角度问题？应该是范围需要考虑是 角度还是弧度 - - -老师的输入是deg or rad -what's my output - - -using deg to get the rate -and feed the rate to the env -""" def test_run_model(): diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/robot.py b/raisimGymTorch/raisimGymTorch/env/deploy/robot.py deleted file mode 100644 index f164332b1..000000000 --- a/raisimGymTorch/raisimGymTorch/env/deploy/robot.py +++ /dev/null @@ -1,8 +0,0 @@ -from math import cos -import numpy as np -T=100 -x = [i for i in range(4 * T)] -y = [1.2 * 10 *(-cos(3.14 * 2 * (i % T) /T) + 1) /2 for i in x] -import matplotlib.pyplot as plt -plt.plot(x,y) -plt.show() \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index e373474c7..15f06e3bc 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -6,8 +6,8 @@ from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO -from raisimGymTorch.env.deploy.angle_utils import transfer_f ,get_last_position -from raisimGymTorch.env.deploy.onnx_deploy import run_model_with_pt_input_modify, list_pt, run_model_with_pt_input +from raisimGymTorch.env.deploy.angle_utils import get_last_position +from raisimGymTorch.env.deploy.onnx_deploy import run_model_with_pt_input_modify, list_pt import os import math import time @@ -21,19 +21,10 @@ from raisimGymTorch.deploy_log.draw_map import Drawer + # task specification task_name = "rsg_test" - -""" -todo: - 1. init model - 2. change p-d info - 3. debug and add_on sine_gait - -""" - - # configuration parser = argparse.ArgumentParser() parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') @@ -117,22 +108,16 @@ his_util=[0] * 12 check_done = lambda a, b: a + 1 if not b else 0 check_history = lambda a, b: a if not b else his_util -# need_to_review = lambda a,b: a if not b else np.array([[0, 0.523, -1.046] * 4]) # todo make sure the shape -# if load_best == True: -# weight_path = "" + print = logger.info -env_idx = np.zeros(100) +env_idx = np.zeros(num_envs) mode == 'train' history_act = np.array([his_util] * num_envs) history_act_0 = np.array([his_util] * num_envs) total_update = args.update if mode =='train' or mode == 'retrain': - # print('start train') schedule = cfg['environment']['schedule'] - angle_rate = cfg['environment']['angle_rate'] - act_rate = cfg['environment']['action_std'] # how many action generated use for work - act_rate = float(act_rate) for update in range(total_update): start = time.time() env.reset() @@ -152,12 +137,6 @@ 'critic_architecture_state_dict': critic.architecture.state_dict(), 'optimizer_state_dict': ppo.optimizer.state_dict(), }, saver.data_dir+"/full_"+str(update)+'.pt') - # we create another graph just to demonstrate the save/load method - # loaded_graph = ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim) - # loaded_graph.load_state_dict(torch.load(saver.data_dir+"/full_"+str(update)+'.pt')['actor_architecture_state_dict']) - - # debug_draw_action = Drawer('debug_draw_action') - # debug_draw_history = Drawer('debug_draw_history') env.turn_on_visualization() env.start_video_recording(datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + "policy_"+str(update)+'.mp4') @@ -167,110 +146,39 @@ with torch.no_grad(): waiter.wait() frame_start = time.time() - obs = env.observe(False) - # print(obs.shape) - # history_act = get_last_position(obs) - # history_act = np.array([ check_history(history_act[i], dones[i]) for i in range(num_envs)] ) - # time.sleep(1) + obs = env.observe(False) action = ppo.act(obs) - aa = action.copy() - aa = np.clip(aa-1, -1, 1) # todo - # action = action.cpu().detach().numpy() - - # sine = sine_generator(envs_idx, schedule, angle_rate) - # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) - # action = action.astype(np.float32) - # -#todo - aa, history_act = run_model_with_pt_input_modify(aa, envs_idx, schedule, history_act) + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + reward, dones = env.step(action) - - - - # draw_his.add_map_list(history_act[0]) - # - # history_act = history_act_0 - # print(history_act) - # debug_draw_action.add_map_list(action[0]) - # debug_draw_history.add_map_list(history_act[0]) - - reward, dones = env.step(aa) - - # history_act = sine - # envs_idx = list(map(check_done, envs_idx, dones)) - # history_act = action - - - # todo history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) - # envs_idx = list(map(check_done, envs_idx, dones)) envs_idx = np.where(dones ==1, 0, envs_idx+1) reward_analyzer.add_reward_info(env.get_reward_info()) frame_end = time.time() wait_time = cfg['environment']['control_dt'] - (frame_end-frame_start) - # if wait_time > 0.: - # time.sleep(wait_time) - # draw_his.draw() + env.stop_video_recording() env.turn_off_visualization() history_act = np.array([his_util] * num_envs) reward_analyzer.analyze_and_plot(update) env.reset() env.save_scaling(saver.data_dir, str(update)) - - # debug_draw_action.draw() - # debug_draw_history.draw() - - # actual training - # sine = [0] * 12 envs_idx = env_idx history_act=history_act_0 for step in range(n_steps): obs = env.observe(False) - """ - 1. z轴方向的加速度的处理方法 - - """ - # history_act = get_last_position(obs) - action = ppo.act(obs) - aa = action.copy() - aa = np.clip(aa - 1, -1, 1) # todo - # if step== 100: - # print(action) - - # sine = sine_generator(envs_idx, schedule, angle_rate) - # action, history_act = transfer_f(action, sine, act_rate, history_act=history_act) - # action = action.astype(np.float32) - # action1, history_act1 = run_model_with_pt_input(action,envs_idx,schedule,history_act) - - aa, history_act = run_model_with_pt_input_modify(aa, envs_idx, schedule, history_act) - - # history_act = history_act_0 - - # print(f'action1 {action1}\n action{action}') - # print(f'history1 {history_act1} \n history{history_act}') - # input() - # print(f'3{time.time()}') - - reward, dones = env.step(aa) - # input('1') - # print(f"history : {history_act}") - - # history_act = sine - # envs_idx = list(map(check_done, envs_idx, dones)) - # history_act = action - + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + reward, dones = env.step(action) history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) - - # reward = reward * envs_idx / n_steps * 2 envs_idx = np.where(dones == 1, 0, envs_idx + 1) - - # envs_idx = list(map(check_done, envs_idx, dones)) ppo.step(value_obs=obs, rews=reward, dones=dones) + + + done_sum = done_sum + np.sum(dones) reward_sum = reward_sum + np.sum(reward) reward_analyzer.add_reward_info(env.get_reward_info()) @@ -279,6 +187,8 @@ obs = env.observe(False) ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) average_ll_performance = reward_sum / total_steps + + if average_ll_performance > biggest_reward: biggest_iter = update biggest_reward = average_ll_performance @@ -309,29 +219,7 @@ print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) * cfg['environment']['control_dt']))) print('----------------------------------------------------\n') -else: - env.reset() - start = time.time() - onnx_flag = False - if onnx_flag: - cnt_onnx = 0 - from raisimGymTorch.env.deploy import onnx_deploy - - # load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) - for step in range(n_steps * 10): - time.sleep(0.01) - obs = env.observe(False) - # print(obs.shape) - if onnx_flag: - action = onnx_deploy.run_model(obs, cnt_onnx, 50) - action = np.array(action)[0] - cnt_onnx += 1 - # action = np.array([act for x in range(100)]) - else: - action = ppo.act(obs) - - reward, dones = env.step(action) print(f'biggest:{biggest_reward},rate = {biggest_iter}') diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 33517884f..38d619c40 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -23,18 +23,6 @@ # from sine_generator import sine_generator from unitree_deploy.angle_utils import sine_generator from raisimGymTorch.env.deploy.angle_utils import deg_rad, rad_deg -"""" -todo - 1. left-hand right-hand check - 2. raisim data check - - env cannot get linear-acc - -""" - - - - # task specification task_name = "sim2real" @@ -52,8 +40,6 @@ mode = args.mode weight_path = args.weight cfg_path = args.cfg_path -# load_best = args.load_best -# check if gpu is available device = torch.device('cpu') # directories @@ -97,8 +83,6 @@ save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) logger = RaisimLogger(saver.data_dir+"/train.log") -if mode =='train' or mode == 'retrain': - tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update num_envs = cfg['environment']['num_envs'] ppo = PPO.PPO(actor=actor, critic=critic, diff --git a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py index a747eae1e..d6d1e8fad 100755 --- a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py +++ b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py @@ -63,17 +63,17 @@ class RaisimLogger(): def __init__(self, path): self.logger = logging.getLogger(__file__) self.logger.setLevel(logging.INFO) - # self.fh = logging.FileHandler(path) + self.fh = logging.FileHandler(path) self.ch = logging.StreamHandler() - # self.fh.setLevel(logging.INFO) + self.fh.setLevel(logging.INFO) self.ch.setLevel(logging.INFO) self.formatter = logging.Formatter("%(asctime)s.%(msecs)03d - %(levelname)s - %(message)s", datefmt='%Y-%m-%d,%H:%M:%S') self.ch.setFormatter(self.formatter) - # self.fh.setFormatter(self.formatter) + self.fh.setFormatter(self.formatter) self.logger.addHandler(self.ch) - # self.logger.addHandler(self.fh) + self.logger.addHandler(self.fh) def info(self, msg): self.logger.info(msg) diff --git a/raisimUnity/linux/.gitignore b/raisimUnity/linux/.gitignore old mode 100644 new mode 100755 From 23b563d3cbbb21d9ce25f25a60a1903e8aeb8bdd Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Mon, 21 Aug 2023 12:19:47 +0800 Subject: [PATCH 29/62] change the file-structure next to make the wheel model --- .../deploy_utils/angle_utils.py | 58 ++++++++ .../data/Environment.hpp | 0 .../deploy => deploy_utils}/onnx_deploy.py | 6 +- .../deploy_utils/thread_protect.py | 27 ++++ .../raisimGymTorch/env/auto_runner.py | 2 +- .../raisimGymTorch/env/deploy/angle_utils.py | 125 ------------------ .../envs/rsg/Environment_tobuildforpython.hpp | 68 +++++----- .../raisimGymTorch/env/envs/rsg/runner.py | 4 +- .../env/envs/rsg/sim_with_real.py | 10 +- .../helper/raisim_gym_helper.py | 10 +- 10 files changed, 137 insertions(+), 173 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/deploy_utils/angle_utils.py rename raisimGymTorch/raisimGymTorch/{env/deploy => deploy_utils}/data/Environment.hpp (100%) rename raisimGymTorch/raisimGymTorch/{env/deploy => deploy_utils}/onnx_deploy.py (98%) create mode 100644 raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py delete mode 100644 raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/angle_utils.py b/raisimGymTorch/raisimGymTorch/deploy_utils/angle_utils.py new file mode 100644 index 000000000..e5021ef50 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/angle_utils.py @@ -0,0 +1,58 @@ +from math import sin,pi +import numpy as np +# from onnx_deploy import rad_deg, deg_rad +from raisimGymTorch.deploy_log.draw_map import Drawer +def deg_rad(x): + if isinstance(x, list): + return [deg_rad(a) for a in x] + else: + return x / 180 * pi + +def rad_deg(x): + if isinstance(x, list): + return [rad_deg(a) for a in x] + else: + return x / pi * 180 + + +low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] +upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] +low = deg_rad(low) +upp = deg_rad(upp) +bund = [upp[i] - low[i] for i in range(12)] +def transfer(act_gen, sine, k, history_act = None) ->np.array: + """ + clip, lerp, generate the final action + params: act_gen: [12 * (-1, 1)] np.array + params: sine: [12 * rad_target] np.array + """ + act_gen = act_gen * bund # - + + act_gen = np.clip(act_gen, low, upp) + if history_act is not None: + kk = 0.9 + act_gen = act_gen * (1-kk) + history_act * kk + act_gen = np.clip(act_gen, low, upp) + if act_gen.shape[0] == 1: + act_gen = act_gen[0] + action = act_gen * k + sine + # print(np.abs(act_gen).max(), sine.max()) + action = np.clip(action, low, upp) + return action + + +def get_last_position(obs): + if isinstance(obs, list): + x = [obs[-2 * i] for i in range(1, 13)] + x.reverse() + return x + elif isinstance(obs, np.ndarray): + x = [] + for i in range(1, 13): + x.append(obs[:, -2*i]) + x.reverse() + return np.stack(x, axis=1) + +if __name__=='__main__': + a = np.random.rand(2,30) + print(a) + print(get_last_position(a).shape) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/data/Environment.hpp b/raisimGymTorch/raisimGymTorch/deploy_utils/data/Environment.hpp similarity index 100% rename from raisimGymTorch/raisimGymTorch/env/deploy/data/Environment.hpp rename to raisimGymTorch/raisimGymTorch/deploy_utils/data/Environment.hpp diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py b/raisimGymTorch/raisimGymTorch/deploy_utils/onnx_deploy.py similarity index 98% rename from raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py rename to raisimGymTorch/raisimGymTorch/deploy_utils/onnx_deploy.py index 785f9059f..62849eff3 100644 --- a/raisimGymTorch/raisimGymTorch/env/deploy/onnx_deploy.py +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/onnx_deploy.py @@ -1,11 +1,11 @@ import time import onnxruntime as ort -# import onnx +# import onnx_file # from mlprodict.onnxrt import OnnxInference from math import cos,pi import numpy as np -from raisimGymTorch.env.deploy.angle_utils import deg_rad, rad_deg +from raisimGymTorch.deploy_utils.angle_utils import deg_rad, rad_deg from raisimGymTorch.deploy_log.draw_map import Drawer # 加载模型 @@ -70,7 +70,7 @@ def deg_normalize(lower, upper, x): history_u = [0] * 12 # history_u_rad = [0] * 12/ # history_u = u0.copy() -model = ort.InferenceSession('/home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/deploy/2203.onnx') +model = ort.InferenceSession('/env/deploy_utils/onnx_file/2203.onnx') idx_local = 0 diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py b/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py new file mode 100644 index 000000000..1bdee005f --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py @@ -0,0 +1,27 @@ +import concurrent.futures +import threading +import time +import numpy as np +from raisimGymTorch.deploy_log.csv_saver import CSV_saver +def cal(): + print('calculating model') + time.sleep(0.3) + + +def save(saver:CSV_saver, data): + print('add data') + saver.add_list(data) + + +if __name__=='__main__': + print('start') + + csv = CSV_saver('./test.csv') + + for i in range(100): + data = np.random.rand(10) + cal() + print(data) + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as exec: + exec.submit(save, data) + print('running robot') \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/auto_runner.py b/raisimGymTorch/raisimGymTorch/env/auto_runner.py index c655590a6..5cf11d923 100644 --- a/raisimGymTorch/raisimGymTorch/env/auto_runner.py +++ b/raisimGymTorch/raisimGymTorch/env/auto_runner.py @@ -23,4 +23,4 @@ def change_data(path, flag, data): else: os.system(f'python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py -u {length} |grep biggest' ) -# os.system('python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py -u 3 |grep biggest' ) +# os.system('python /home/lr-2002/code/raisimLib/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner_util.py -u 3 |grep biggest' ) diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py b/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py deleted file mode 100644 index 2b6a31acf..000000000 --- a/raisimGymTorch/raisimGymTorch/env/deploy/angle_utils.py +++ /dev/null @@ -1,125 +0,0 @@ -from math import sin,pi -import numpy as np -# from onnx_deploy import rad_deg, deg_rad -from raisimGymTorch.deploy_log.draw_map import Drawer -def deg_rad(x): - if isinstance(x, list): - return [deg_rad(a) for a in x] - else: - return x / 180 * pi - -def rad_deg(x): - if isinstance(x, list): - return [rad_deg(a) for a in x] - else: - return x / pi * 180 - - -low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] -upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] -low = deg_rad(low) -upp = deg_rad(upp) -bund = [upp[i] - low[i] for i in range(12)] -def sine_generator(angle_list, idx, T, rate=1): - base1= 0.523 - base3= 0.0 - base2 = -2 * base1 - ang = abs(sin( float(idx) / T * pi)) * rate - - idx_base = 0 - - if (int(idx/T) % 2 ) == 1: - angle_list[idx_base+1] = ang + base1 - angle_list[idx_base+2] = -2 * ang + base2 - - angle_list[idx_base+4] = base1 - angle_list[idx_base+5] = base2 - - angle_list[idx_base+7] = base1 - angle_list[idx_base+8] = base2 - angle_list[idx_base+10] = ang + base1 - angle_list[idx_base+11] = - 2 * ang + base2 - else: - angle_list[idx_base+1] = base1 - angle_list[idx_base+2] = base2 - - angle_list[idx_base+10] = base1 - angle_list[idx_base+11] = base2 - angle_list[idx_base+4] = ang + base1 - angle_list[idx_base+5] = -2 * ang + base2 - - angle_list[idx_base+7] = ang + base1 - angle_list[idx_base+8] = -2 * ang + base2 - angle_list[idx_base+0] = base3 - angle_list[idx_base+3] = base3 - angle_list[idx_base+6] = base3 - angle_list[idx_base+9] = base3 - return angle_list - -def transfer(act_gen, sine, k, history_act = None) ->np.array: - """ - clip, lerp, generate the final action - params: act_gen: [12 * (-1, 1)] np.array - params: sine: [12 * rad_target] np.array - """ - # act_gen = (act_gen + 1) /2 # 0-1 - act_gen = act_gen * bund # - + - # print(act_gen) - act_gen = np.clip(act_gen, low, upp) - if history_act is not None: - kk = 0.9 - # act_gen = (1-kk) * act_gen - act_gen = act_gen * (1-kk) + history_act * kk - act_gen = np.clip(act_gen, low, upp) - if act_gen.shape[0] == 1: - act_gen = act_gen[0] - action = act_gen * k + sine - # print(np.abs(act_gen).max(), sine.max()) - action = np.clip(action, low, upp) - return action - -def transfer_f(act_gen, sine, k, history_act = None) ->np.array: - """ - clip, lerp, generate the final action - params: act_gen: [12 * (-1, 1)] np.array - params: sine: [12 * rad_target] np.array - """ - # act_gen = (act_gen + 1) /2 # 0-1 - act_gen = act_gen * bund / 2 # - + - # print(act_gen) - act_gen = np.clip(act_gen, low, upp) - if history_act is not None: - kk = 1-k - # act_gen = (1-kk) * act_gen - # print(f'his1 {history_act}\n act_gen{act_gen}') - history_act = act_gen * (1-kk) + history_act * kk - history_act = np.clip(history_act, low, upp) - # print(f'his2 {history_act}') - - if history_act.shape[0] == 1: - tmp = history_act[0] - else: - tmp = history_act - action = tmp * 0.2 + sine - # print(np.abs(act_gen).max(), sine.max()) - action = np.clip(action, low, upp) - # print(f'bound{bund}\n act_gen{act_gen}\n history_act{history_act}\n sine{sine} \naction{action}\nk{k} ') - # input('1') - return action, history_act - -def get_last_position(obs): - if isinstance(obs, list): - x = [obs[-2 * i] for i in range(1, 13)] - x.reverse() - return x - elif isinstance(obs, np.ndarray): - x = [] - for i in range(1, 13): - x.append(obs[:, -2*i]) - x.reverse() - return np.stack(x, axis=1) - -if __name__=='__main__': - a = np.random.rand(2,30) - print(a) - print(get_last_position(a).shape) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp index 6c0643880..6fa5d0d06 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp @@ -57,7 +57,7 @@ namespace raisim { skate = world_ ->addArticulatedSystem("/home/lr-2002/code/raisimLib/rsc/skate/skate.urdf"); anymal_->setName("model"); anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); - world_->addGround(); + world_->addGround(0,"land"); /// get robot data gcDim_ = anymal_->getGeneralizedCoordinateDim(); // 19 @@ -65,7 +65,7 @@ namespace raisim { nJoints_ = gvDim_ - 6; skate_posi_init.setZero(9); skate_vel_init.setZero(8); - skate_posi_init << 0, 0.12, 0.11, 0.707, 0., 0, 0.707, 0 , 0; + skate_posi_init << 0, 0.15, 0.11, 0.707, 0., 0, 0.707, 0 , 0; skate ->setGeneralizedCoordinate(skate_posi_init); gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); @@ -78,11 +78,9 @@ namespace raisim { skate_vel_.setZero(8); skate_posi_.setZero(9); // init_position(); -double aa = double(30) /180*PI, bb = double(60) /180*PI; +double aa = 0.5233 , bb = double(60) /180*PI; gc_init_<< 0, 0, 0.35, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa,-2*aa, 0.0, bb, -2*bb; - // gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; -// init_position(gc_init_); init(); obDim_ = 29; @@ -101,7 +99,25 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; footIndices_.insert(anymal_->getBodyIdx("RL_calf")); footIndices_.insert(anymal_->getBodyIdx("RR_calf")); - /// visualize if it is the first environment + for(auto n : anymal_->getCollisionBodies()) + { + auto name = n.colObj->name; + anymal_->getCollisionBody(name).setMaterial("steel"); + } + + skate->getCollisionBody("base/0").setMaterial("sandpaper"); + skate->getCollisionBody("rotater_r/0").setMaterial("rubber"); + skate->getCollisionBody("rotater_f/0").setMaterial("rubber"); + anymal_->getCollisionBody("FL_foot/0").setMaterial("rubber"); + anymal_->getCollisionBody("FR_foot/0").setMaterial("rubber"); + anymal_->getCollisionBody("RR_foot/0").setMaterial("rubber"); + anymal_->getCollisionBody("RL_foot/0").setMaterial("rubber"); + world_->setMaterialPairProp("steel", "rubber", 0.8, 0.15, 0.001); + world_->setMaterialPairProp("rubber", "sandpaper", 0.99, 0.15, 0.001); + world_->setMaterialPairProp("land", "rubber", 0.8, 0.1,0.001); + world_->setMaterialPairProp("sandpaper", "land", 0.4, 0.15,0.001); + world_->setMaterialPairProp("steel","land", 0.1, 0.05,0.001); + if (visualizable_) { server_ = std::make_unique(world_.get()); server_->launchServer(); @@ -115,19 +131,9 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(p_gain); -// jointPgain.tail(nJoints_)[2] = 300; -// jointPgain.tail(nJoints_)[5] = 300; -// jointPgain.tail(nJoints_)[8] = 300; -// jointPgain.tail(nJoints_)[11] = 300; jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(d_gain); -// jointDgain.tail(nJoints_)[2] = 15; -// jointDgain.tail(nJoints_)[5] = 15; -// jointDgain.tail(nJoints_)[8] = 15; -// jointDgain.tail(nJoints_)[11] = 15; anymal_->setPdGains(jointPgain, jointDgain); -// std::cout<setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); if(show_ref) { @@ -137,7 +143,6 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); anymal_1->setPdGains(jointPgain,jointDgain); } -// std::cout<<"position inited\n"; } void init_position(const Eigen::Ref& posi) final @@ -183,21 +188,17 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; } updateObservation(); double rrr =0; -// for(int i=0;i<=2;i++) rrr += abs(euler_angle[i]) ; rrr = abs(euler_angle[0]) + abs(euler_angle[1]) + abs(euler_angle[2]); rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1]) +abs(ang_vel_[2])); -// rrr = abs(gc_[0]) + abs(gc_[1]); -// rrr += (gc_.tail(12) - pTarget12_).norm() ; + rrr += abs(gc_[0] - skate_posi_[0]) + abs(gc_[1] - (skate_posi_[1] - 0.15)); bool accu = false; rewards_.record("Stable",-rrr, accu); -// std::cout<<"eu 0 "<< euler_angle[0] << " eu1 " << euler_angle[1] << std::endl; rewards_.record("Live", 1, accu); rewards_.record("forwardVel", skate_vel_[0], accu); rewards_.record("height", 0.45- abs(gc_[2] - 0.45) - abs(gc_[0] ) - abs(gc_[1]) , accu); // rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); // rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); -// euler_angle_old = euler_angle; rewards_.record("torque",anymal_->getGeneralizedForce().squaredNorm() ); return rewards_.sum(); } @@ -205,6 +206,7 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; void updateObservation() { anymal_->getState(gc_, gv_); skate -> getState(skate_posi_, skate_vel_); + raisim::Vec<4> quat; raisim::Mat<3,3> rot; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; @@ -213,14 +215,8 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); anymal_->getAngularVelocity(anymal_->getBodyIdx("base"), ang_vel_); -// anymal_->getVelocity(anymal_->getBodyIdx("base"), bodyLinearVel_); -// anymal_->get Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); euler_angle = ToEulerAngles(qua); -// if(euler_angle[1] > PI) euler_angle[1] -= 2*PI; -// if(euler_angle[0] > PI) euler_angle[0] -= 2*PI; -// if(euler_angle[1] < -PI) euler_angle[1] += 2 * PI; -// if(euler_angle[0] < -PI) euler_angle[0] += 2 * PI; Eigen::VectorXd gcc = gc_.tail(12); Eigen::VectorXd gvv = gv_.tail(12); Eigen::VectorXd c_v(24); @@ -238,8 +234,6 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; ang_vel_[1], ang_vel_[2], c_v; -// std::cout<<"ang_vel : " << ang_vel_ << std::endl; - } @@ -253,17 +247,15 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; // for(auto& contact: anymal_->getContacts()) // if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) // {// if there is any contact body was not in the footIndices the over -//// rewards_.record("Live", terminalReward, accu); -//// std::cout<<"foot done " << std::endl; +// rewards_.record("Live", terminalReward, accu); +// std::cout<<"foot done " << std::endl; // return true;} if(gc_[2] - gc_init_[2] > 0.3){ // std::cout<<"z done" << std::endl; // rewards_.record("Live", terminalReward, accu); - return true; } - if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.3) { // std::cout<<"y angle done" <<" " << euler_angle[1]<< std::endl; @@ -278,6 +270,14 @@ double aa = double(30) /180*PI, bb = double(60) /180*PI; // return true; } + if(abs(gc_[0] - skate_posi_[0]) >0.2) + { + return true; + } + if(abs(gc_[1] - (skate_posi_[1] - 0.15)) >0.1) + { + return true; + } terminalReward = 0.f; return false; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 15f06e3bc..3eac3c551 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -6,8 +6,8 @@ from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO -from raisimGymTorch.env.deploy.angle_utils import get_last_position -from raisimGymTorch.env.deploy.onnx_deploy import run_model_with_pt_input_modify, list_pt +from raisimGymTorch.deploy_utils.angle_utils import get_last_position +from raisimGymTorch.deploy_utils.runner_util import run_model_with_pt_input_modify, list_pt import os import math import time diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py index 38d619c40..11bfb70df 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/sim_with_real.py @@ -6,9 +6,9 @@ from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer import raisimGymTorch.algo.ppo.module as ppo_module import raisimGymTorch.algo.ppo.ppo as PPO -from raisimGymTorch.env.deploy.angle_utils import transfer +from raisimGymTorch.env.deploy_util.angle_utils import transfer from unitree_utils.Waiter import Waiter -from raisimGymTorch.env.deploy.angle_utils import get_last_position +from raisimGymTorch.deploy_utils.angle_utils import get_last_position from raisimGymTorch.deploy_log.draw_map import Drawer from raisimGymTorch.deploy_log.csv_saver import CSV_saver import os @@ -22,7 +22,7 @@ # import pandas as pd # from sine_generator import sine_generator from unitree_deploy.angle_utils import sine_generator -from raisimGymTorch.env.deploy.angle_utils import deg_rad, rad_deg +from raisimGymTorch.deploy_utils.angle_utils import deg_rad, rad_deg # task specification task_name = "sim2real" @@ -138,7 +138,7 @@ onnx_flag = True if onnx_flag: cnt_onnx = 0 - from raisimGymTorch.env.deploy import onnx_deploy + from raisimGymTorch.env.deploy_util import onnx_deploy else: load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) @@ -152,7 +152,7 @@ history_obs = None if onnx_flag: cnt_onnx = 0 - from raisimGymTorch.env.deploy import onnx_deploy + from raisimGymTorch.env.deploy_util import onnx_deploy from robot_utils import * a1.torque_limit = 33.15 init_robot(dt) diff --git a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py index d6d1e8fad..16aea52d6 100755 --- a/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py +++ b/raisimGymTorch/raisimGymTorch/helper/raisim_gym_helper.py @@ -65,8 +65,8 @@ def __init__(self, path): self.logger.setLevel(logging.INFO) self.fh = logging.FileHandler(path) self.ch = logging.StreamHandler() - self.fh.setLevel(logging.INFO) - self.ch.setLevel(logging.INFO) + self.fh.setLevel(logging.DEBUG) + self.ch.setLevel(logging.DEBUG) self.formatter = logging.Formatter("%(asctime)s.%(msecs)03d - %(levelname)s - %(message)s", datefmt='%Y-%m-%d,%H:%M:%S') @@ -82,4 +82,8 @@ def warning(self, msg): def debug(self, msg): self.logger.debug(msg) def error(self, msg): - self.logger.error(msg) \ No newline at end of file + self.logger.error(msg) + + def setLevel(self, Level): + self.fh.setLevel(Level) + self.ch.setLevel(Level) \ No newline at end of file From 1587f1891ee5a72163cb5badff451679cc523c34 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Mon, 21 Aug 2023 14:57:32 +0800 Subject: [PATCH 30/62] real-time training without threading --- raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py | 4 +- .../deploy_utils/thread_protect.py | 12 +- ...ildforpython.hpp => Environment_skate.hpp} | 0 .../env/envs/rsg/Environment_wheel.hpp | 316 ++++++++++++++++++ .../raisimGymTorch/env/envs/rsg/cfg.yaml | 16 +- .../raisimGymTorch/env/envs/rsg/on_p_train.py | 189 +++++++++++ .../raisimGymTorch/env/envs/rsg/runner.py | 8 +- .../raisimGymTorch/env/envs/rsg_go1/runner.py | 2 +- .../raisimGymTorch/env/raisim_gym.cpp | 2 +- 9 files changed, 531 insertions(+), 18 deletions(-) rename raisimGymTorch/raisimGymTorch/env/envs/rsg/{Environment_tobuildforpython.hpp => Environment_skate.hpp} (100%) create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py diff --git a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py index 20c802822..292f3e164 100755 --- a/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py +++ b/raisimGymTorch/raisimGymTorch/algo/ppo/ppo.py @@ -77,8 +77,8 @@ def act(self, actor_obs): self.actor_obs = actor_obs with torch.no_grad(): self.actions, self.actions_log_prob = self.actor.sample(torch.from_numpy(actor_obs).to(self.device)) - # self.actions = np.clip(self.actions - 1, -1, 1) - return self.actions + actions = np.clip(self.actions - 1, -1, 1) + return actions def forward(self, actor_obs): return self.act(actor_obs) diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py b/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py index 1bdee005f..8c5760b51 100644 --- a/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/thread_protect.py @@ -5,23 +5,25 @@ from raisimGymTorch.deploy_log.csv_saver import CSV_saver def cal(): print('calculating model') - time.sleep(0.3) + time.sleep(0.1) def save(saver:CSV_saver, data): - print('add data') + # print('add data') saver.add_list(data) + print(len(saver.csv_list)) + saver.save() if __name__=='__main__': print('start') - csv = CSV_saver('./test.csv') + csv = CSV_saver('./test') for i in range(100): - data = np.random.rand(10) + data = np.zeros((1000,10)) cal() - print(data) + # print(data) with concurrent.futures.ThreadPoolExecutor(max_workers=1) as exec: exec.submit(save, data) print('running robot') \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_skate.hpp similarity index 100% rename from raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_tobuildforpython.hpp rename to raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_skate.hpp diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp new file mode 100644 index 000000000..5f5c749f8 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp @@ -0,0 +1,316 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#include +#define PI 3.1415926 + +namespace raisim { + + auto ToEulerAngles(Eigen::Quaterniond q) { + Eigen::Vector3d angles; + + // roll (x-axis rotation) + double sinr_cosp = 2 * (q.w() * q.x() + q.y() * q.z()); + double cosr_cosp = 1 - 2 * (q.x ()* q.x() + q.y() * q.y()); + angles[0] = std::atan2(sinr_cosp, cosr_cosp); + + // pitch (y-axis rotation) + double sinp = std::sqrt(1 + 2 * (q.w ()* q.y ()- q.x() * q.z())); + double cosp = std::sqrt(1 - 2 * (q.w ()* q.y() - q.x() * q.z())); + angles[1] = 2 * std::atan2(sinp, cosp) - M_PI / 2; + + // yaw (z-axis rotation) + double siny_cosp = 2 * (q.w() * q.z() + q.x() * q.y()); + double cosy_cosp = 1 - 2 * (q.y() * q.y() + q.z() * q.z()); + angles[2] = std::atan2(siny_cosp, cosy_cosp); + + return angles; + } + + class ENVIRONMENT : public RaisimGymEnv { + + public: + + explicit ENVIRONMENT(const std::string& resourceDir, const Yaml::Node& cfg, bool visualizable) : + RaisimGymEnv(resourceDir, cfg), visualizable_(visualizable), normDist_(0, 1) { + /// create world + world_ = std::make_unique(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + READ_YAML(std::string, urdf_path, cfg_["urdf_path"]); + READ_YAML(double, p_gain, cfg_["p_gain"]); + READ_YAML(double, d_gain, cfg_["d_gain"]); + anymal_ = world_->addArticulatedSystem(urdf_path); +// skate = world_ ->addArticulatedSystem("/home/lr-2002/code/raisimLib/rsc/skate/skate.urdf"); + anymal_->setName("model"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(0,"land"); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); // 19 + gvDim_ = anymal_->getDOF(); // 18 + nJoints_ = gvDim_ - 6; +// skate_posi_init.setZero(9); +// skate_vel_init.setZero(8); +// skate_posi_init << 0, 0.15, 0.11, 0.707, 0., 0, 0.707, 0 , 0; +// skate ->setGeneralizedCoordinate(skate_posi_init); + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + euler_angle.setZero();euler_angle_old.setZero(); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + acc_.setZero();//1 + ang_vel_.setZero(); +// skate_vel_.setZero(8); +// skate_posi_.setZero(9); +// init_position(); +double aa = 0.5233 , bb = double(30) /180*PI; + gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa,-2*aa, 0.0, bb, -2*bb; +// gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; + init(); + + obDim_ = 29; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + + actionStd_.setConstant(action_std); + + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + for(auto n : anymal_->getCollisionBodies()) + { + auto name = n.colObj->name; + anymal_->getCollisionBody(name).setMaterial("steel"); + } + +// skate->getCollisionBody("base/0").setMaterial("sandpaper"); +// skate->getCollisionBody("rotater_r/0").setMaterial("rubber"); +// skate->getCollisionBody("rotater_f/0").setMaterial("rubber"); + anymal_->getCollisionBody("FL_foot/0").setMaterial("rubber"); + anymal_->getCollisionBody("FR_foot/0").setMaterial("rubber"); + anymal_->getCollisionBody("RR_foot/0").setMaterial("rubber"); + anymal_->getCollisionBody("RL_foot/0").setMaterial("rubber"); + world_->setMaterialPairProp("steel", "rubber", 0.8, 0.15, 0.001); + world_->setMaterialPairProp("rubber", "sandpaper", 0.99, 0.15, 0.001); + world_->setMaterialPairProp("land", "rubber", 0.8, 0.1,0.001); + world_->setMaterialPairProp("sandpaper", "land", 0.4, 0.15,0.001); + world_->setMaterialPairProp("steel","land", 0.1, 0.05,0.001); + + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + + + void init() final { + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); + jointPgain.tail(nJoints_).setConstant(p_gain); + jointDgain.setZero(); + jointDgain.tail(nJoints_).setConstant(d_gain); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(urdf_path); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + } + + void init_position(const Eigen::Ref& posi) final + { + gc_init_ = posi.cast(); + init(); + } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); +// skate->setState(skate_posi_init, skate_vel_init); + rewards_.setZero(); + COUNT=0; +// euler_angle_old.setZero(); + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + if (show_ref) + { + Eigen::Vector3d po(3, 3 ,10); + anymal_1->setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + } + COUNT ++; + pTarget12_ = action.cast(); + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + updateObservation(); + double rrr =0; + rrr = abs(euler_angle[0]) + abs(euler_angle[1]) ; + rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1])); +// rrr += abs(gc_[0] - skate_posi_[0]) + abs(gc_[1] - (skate_posi_[1] - 0.15)); + bool accu = false; + rewards_.record("Stable",-rrr, accu); + rewards_.record("Live", 1, accu); +// rewards_.record("forwardVel", skate_vel_[0], accu); +// rewards_.record("height", 0.45- abs(gc_[2] - 0.45) - abs(gc_[0] ) - abs(gc_[1]) , accu); +// rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); +// rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); + rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); + rewards_.record("torque",anymal_->getGeneralizedForce().squaredNorm() ); + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); +// skate -> getState(skate_posi_, skate_vel_); + + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + quat /= quat.norm(); + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); + anymal_->getAngularVelocity(anymal_->getBodyIdx("base"), ang_vel_); + Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); + euler_angle = ToEulerAngles(qua); + Eigen::VectorXd gcc = gc_.tail(12); + Eigen::VectorXd gvv = gv_.tail(12); + Eigen::VectorXd c_v(24); + c_v.setZero(24); + for(auto i = 0; i <12 ; i++ ) + { + c_v[i*2] = gcc[i]; + c_v[i*2 + 1] = gvv[i]; + } + + obDouble_ << + euler_angle[0], + euler_angle[1],// quaternion + ang_vel_[0], + ang_vel_[1], + ang_vel_[2], + c_v; + + } + + void observe(Eigen::Ref ob) final { + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + bool accu = true; +// for(auto& contact: anymal_->getContacts()) +// if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) +// {// if there is any contact body was not in the footIndices the over +// rewards_.record("Live", terminalReward, accu); +// std::cout<<"foot done " << std::endl; +// return true;} +// if(gc_[2] - gc_init_[2] > 0.3){ +// std::cout<<"z done" << std::endl; +// rewards_.record("Live", terminalReward, accu); +// return true; +// } + +// if(fmin(abs(euler_angle[1]), abs(euler_angle[1] + 2 * PI)) > 0.3) +// { +// std::cout<<"y angle done" <<" " << euler_angle[1]<< std::endl; +// rewards_.record("Live", terminalReward, accu); + +// return true; +// } +// if(fmin(abs(euler_angle[0]), abs(euler_angle[0] + 2 * PI)) > 0.3) +// { +// std::cout<<"x angle done " << euler_angle[0] << std::endl; +// rewards_.record("Live", terminalReward, accu); +// +// return true; +// } +// if(abs(gc_[0] - skate_posi_[0]) >0.2) +// { +// return true; +// } +// if(abs(gc_[1] - (skate_posi_[1] - 0.15)) >0.1) +// { +// return true; +// } + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Vec<3> acc_, ang_vel_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + double p_gain,d_gain; + std::string urdf_path; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + Eigen::Vector3d euler_angle, euler_angle_old; + /// these variables are not in use. They are placed to show you how to create a random number sampler. + bool float_base; +// Eigen::VectorXd skate_vel_, skate_posi_,skate_posi_init, skate_vel_init; + std::normal_distribution normDist_; + thread_local static std::mt19937 gen_; +}; +thread_local std::mt19937 raisim::ENVIRONMENT::gen_; + +} diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index e0d4d6c53..0740969c9 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -4,9 +4,9 @@ record_video: true environment: render: true # just testing commenting - num_envs: 100 + num_envs: 1 eval_every_n: 40 - num_threads: 30 + num_threads: 1 simulation_dt: 0.0025 control_dt: 0.01 max_time: 4 @@ -23,20 +23,24 @@ environment: urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: forwardVel: - coeff: 0.5 + coeff: 0. torque: coeff: -4e-5 Stable: - coeff: 0.01 + coeff: 0.4 height: coeff: 0. Live: - coeff: 0 + coeff: 1 Wheel: - coeff: 0. + coeff: 0.3 # Mimic: # coeff: 0. architecture: policy_net: [128, 128] value_net: [128, 128] + +on_policy: + rate: 0.6 # for sine A + kb: 0.05 # act_gen * kb diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py new file mode 100755 index 000000000..9b531cbd7 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py @@ -0,0 +1,189 @@ +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger +from raisimGymTorch.env.bin.rsg import NormalSampler +from raisimGymTorch.env.bin.rsg import RaisimGymEnv +from raisimGymTorch.env.RewardAnalyzer import RewardAnalyzer +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO +from raisimGymTorch.deploy_utils.angle_utils import get_last_position +from raisimGymTorch.deploy_utils.runner_util import run_model_with_pt_input_modify, list_pt +import os +import math +import time +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse + +from unitree_utils.Waiter import Waiter +from raisimGymTorch.deploy_log.draw_map import Drawer +from raisimGymTorch.deploy_log.csv_saver import CSV_saver + +# task specification +task_name = "rsg_test" + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=300) +parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +mode = args.mode +weight_path = args.weight +cfg_path = args.cfg_path +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +print(device) +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +if cfg_path is not None: + cfg = YAML().load(open(cfg_path, 'r')) +else: + cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) +print(cfg['environment']) + +# create environment from the configuration file +env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +# shortcuts +ob_dim = env.num_obs +act_dim = env.num_acts +num_threads = cfg['environment']['num_threads'] + +# Training +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * env.num_envs + +avg_rewards = [] + +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + env.num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + +saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, + save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) +logger = RaisimLogger(saver.data_dir+"/train.log") + +if mode =='train' or mode == 'retrain': + tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update +num_envs = cfg['environment']['num_envs'] +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=16, + # learning_rate_schedule='', + device=device, + log_dir=saver.data_dir, + shuffle_batch=False, + # learning_rate=cfg['environment']['learnning_rate'] + ) + +reward_analyzer = RewardAnalyzer(env, ppo.writer) +biggest_reward = 0 +biggest_iter = 0 +if mode == 'retrain': + load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) + +# his_util=[0, 0.523, -1.046] * 4 +his_util=[0] * 12 +check_done = lambda a, b: a + 1 if not b else 0 +check_history = lambda a, b: a if not b else his_util + +save_observe = CSV_saver('./observation_saver') +save_act = CSV_saver('./act_saver') + +print = logger.info +on_p_rate =cfg['on_policy']['rate'] +on_p_kb = cfg['on_policy']['kb'] +env_idx = np.zeros(num_envs) +mode == 'train' +history_act = np.array([his_util] * num_envs) +history_act_0 = np.array([his_util] * num_envs) +total_update = args.update +if mode =='train' or mode == 'retrain': + env.turn_on_visualization() + schedule = cfg['environment']['schedule'] + envs_idx = 0 + waiter = Waiter(0.01) + waiter.update_start() + for update in range(total_update): + # print('after update') + reward_sum = 0 + + for step in range(n_steps): + waiter.wait() + obs = env.observe(False) + action = ppo.act(obs) + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act, kb=on_p_kb, rate=on_p_rate) + reward, _ = env.step(action) + + envs_idx +=1 + ppo.step(value_obs=obs, rews=reward, dones=_) + + save_observe.add_list(obs[0]) + save_act.add_list(action[0]) + + reward_sum = reward_sum + np.sum(reward) + reward_analyzer.add_reward_info(env.get_reward_info()) + reward_analyzer.analyze_and_plot(update) + # take st step to get value obs + print('before update') + obs = env.observe(False) + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) + average_ll_performance = reward_sum / total_steps + + print('before saving') + + if update % cfg['environment']['eval_every_n'] == 0: + biggest_iter = update + biggest_reward = average_ll_performance + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir+"/full_"+str(update)+'.pt') + env.save_scaling(saver.data_dir, str(update)) + save_act.save() + save_observe.save() + print('after save') + + avg_rewards.append(average_ll_performance) + + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) + print('after update') + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + # print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) + # print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) + # print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) + # print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) + # * cfg['environment']['control_dt']))) + print('----------------------------------------------------\n') + print('after print') + env.turn_off_visualization() +print(f'biggest:{biggest_reward},rate = {biggest_iter}') + + diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py index 3eac3c551..4d66c3a34 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/runner.py @@ -16,7 +16,7 @@ import torch import datetime import argparse -from unitree_deploy.angle_utils import sine_generator + from unitree_utils.Waiter import Waiter from raisimGymTorch.deploy_log.draw_map import Drawer @@ -111,6 +111,8 @@ print = logger.info +on_p_rate =cfg['on_policy']['rate'] +on_p_kb = cfg['on_policy']['kb'] env_idx = np.zeros(num_envs) mode == 'train' history_act = np.array([his_util] * num_envs) @@ -150,7 +152,7 @@ obs = env.observe(False) action = ppo.act(obs) - action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act, kb=on_p_kb, rate=on_p_rate) reward, dones = env.step(action) @@ -171,7 +173,7 @@ for step in range(n_steps): obs = env.observe(False) action = ppo.act(obs) - action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act) + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act, kb=on_p_kb, rate=on_p_rate) reward, dones = env.step(action) history_act = np.array([check_history(history_act[i], dones[i]) for i in range(num_envs)]) envs_idx = np.where(dones == 1, 0, envs_idx + 1) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py index 880a89c12..bb92a989a 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg_go1/runner.py @@ -200,7 +200,7 @@ debug = True if onnx_flag: cnt_onnx = 0 - from raisimGymTorch.env.deploy import onnx_deploy + from raisimGymTorch.env.deploy_util import onnx_deploy T = 50 np.set_printoptions(threshold=np.inf) draw_line =[] diff --git a/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp b/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp index 431445229..2ce437582 100755 --- a/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp +++ b/raisimGymTorch/raisimGymTorch/env/raisim_gym.cpp @@ -6,7 +6,7 @@ #include #include #include -#include "Environment_tobuildforpython.hpp" +#include "Environment_wheel.hpp" #include "VectorizedEnvironment.hpp" namespace py = pybind11; From ef6931ad7e23c4b0369e72e224fb9d70e243c931 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Mon, 21 Aug 2023 15:21:36 +0800 Subject: [PATCH 31/62] this version is able to wheel with real time in raisim --- raisimGymTorch/raisimGymTorch/.gitignore | 2 + .../deploy => deploy_utils}/data/full_152.pt | Bin .../deploy => deploy_utils}/data/mean152.csv | 0 .../raisimGymTorch/env/envs/rsg/on_p_train.py | 98 +++++++++--------- 4 files changed, 51 insertions(+), 49 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/.gitignore rename raisimGymTorch/raisimGymTorch/{env/deploy => deploy_utils}/data/full_152.pt (100%) rename raisimGymTorch/raisimGymTorch/{env/deploy => deploy_utils}/data/mean152.csv (100%) diff --git a/raisimGymTorch/raisimGymTorch/.gitignore b/raisimGymTorch/raisimGymTorch/.gitignore new file mode 100644 index 000000000..6321e422d --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/.gitignore @@ -0,0 +1,2 @@ +*.onnx +*.pt diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/data/full_152.pt b/raisimGymTorch/raisimGymTorch/deploy_utils/data/full_152.pt similarity index 100% rename from raisimGymTorch/raisimGymTorch/env/deploy/data/full_152.pt rename to raisimGymTorch/raisimGymTorch/deploy_utils/data/full_152.pt diff --git a/raisimGymTorch/raisimGymTorch/env/deploy/data/mean152.csv b/raisimGymTorch/raisimGymTorch/deploy_utils/data/mean152.csv similarity index 100% rename from raisimGymTorch/raisimGymTorch/env/deploy/data/mean152.csv rename to raisimGymTorch/raisimGymTorch/deploy_utils/data/mean152.csv diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py index 9b531cbd7..c24201e36 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py @@ -1,3 +1,6 @@ +import concurrent.futures +import threading + from ruamel.yaml import YAML, dump, RoundTripDumper from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger @@ -79,6 +82,41 @@ save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) logger = RaisimLogger(saver.data_dir+"/train.log") + + +def updating(): + obs = env.observe(False) + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) + average_ll_performance = reward_sum / total_steps + + + if update % cfg['environment']['eval_every_n'] == 0: + biggest_iter = update + biggest_reward = average_ll_performance + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir + "/full_" + str(update) + '.pt') + env.save_scaling(saver.data_dir, str(update)) + save_act.save() + save_observe.save() + + + avg_rewards.append(average_ll_performance) + + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) + + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + print('----------------------------------------------------\n') + + + if mode =='train' or mode == 'retrain': tensorboard_launcher(saver.data_dir+"/..") # press refresh (F5) after the first ppo update num_envs = cfg['environment']['num_envs'] @@ -90,11 +128,9 @@ gamma=0.996, lam=0.95, num_mini_batches=16, - # learning_rate_schedule='', device=device, log_dir=saver.data_dir, shuffle_batch=False, - # learning_rate=cfg['environment']['learnning_rate'] ) reward_analyzer = RewardAnalyzer(env, ppo.writer) @@ -103,21 +139,16 @@ if mode == 'retrain': load_param(weight_path, env, actor, critic, ppo.optimizer, saver.data_dir) -# his_util=[0, 0.523, -1.046] * 4 -his_util=[0] * 12 -check_done = lambda a, b: a + 1 if not b else 0 -check_history = lambda a, b: a if not b else his_util - save_observe = CSV_saver('./observation_saver') save_act = CSV_saver('./act_saver') - print = logger.info + + on_p_rate =cfg['on_policy']['rate'] on_p_kb = cfg['on_policy']['kb'] -env_idx = np.zeros(num_envs) + mode == 'train' -history_act = np.array([his_util] * num_envs) -history_act_0 = np.array([his_util] * num_envs) + total_update = args.update if mode =='train' or mode == 'retrain': env.turn_on_visualization() @@ -126,7 +157,6 @@ waiter = Waiter(0.01) waiter.update_start() for update in range(total_update): - # print('after update') reward_sum = 0 for step in range(n_steps): @@ -146,43 +176,13 @@ reward_analyzer.add_reward_info(env.get_reward_info()) reward_analyzer.analyze_and_plot(update) # take st step to get value obs - print('before update') - obs = env.observe(False) - ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) - average_ll_performance = reward_sum / total_steps - - print('before saving') - - if update % cfg['environment']['eval_every_n'] == 0: - biggest_iter = update - biggest_reward = average_ll_performance - torch.save({ - 'actor_architecture_state_dict': actor.architecture.state_dict(), - 'actor_distribution_state_dict': actor.distribution.state_dict(), - 'critic_architecture_state_dict': critic.architecture.state_dict(), - 'optimizer_state_dict': ppo.optimizer.state_dict(), - }, saver.data_dir+"/full_"+str(update)+'.pt') - env.save_scaling(saver.data_dir, str(update)) - save_act.save() - save_observe.save() - print('after save') - - avg_rewards.append(average_ll_performance) - - actor.update() - actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) - print('after update') - - print('----------------------------------------------------') - print('{:>6}th iteration'.format(update)) - print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) - # print('{:<40} {:>6}'.format("dones: ", '{:0.6f}'.format(average_dones))) - # print('{:<40} {:>6}'.format("time elapsed in this iteration: ", '{:6.4f}'.format(end - start))) - # print('{:<40} {:>6}'.format("fps: ", '{:6.0f}'.format(total_steps / (end - start)))) - # print('{:<40} {:>6}'.format("real time factor: ", '{:6.0f}'.format(total_steps / (end - start) - # * cfg['environment']['control_dt']))) - print('----------------------------------------------------\n') - print('after print') + update_thread = threading.Thread(target=updating) + update_thread.start() + while update_thread.is_alive(): + waiter.wait() + # print('threading running') + env.step(action) + print('updating finished') env.turn_off_visualization() print(f'biggest:{biggest_reward},rate = {biggest_iter}') From 976ff33458e99a3a5f5bd7be043a3d58383bdd3d Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Mon, 21 Aug 2023 15:22:59 +0800 Subject: [PATCH 32/62] this version is able to wheel with real time in raisim --- .../deploy_utils/runner_util.py | 134 ++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py new file mode 100644 index 000000000..0d420bbca --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py @@ -0,0 +1,134 @@ +import numpy as np +from math import cos,pi +def lerp_np(a, b, c): + return a* (1-c) + b*c +def add_list_np(act_gen, sine, history,kb): + kk = 0.9 + kf = 1 + kb = kb + history = history*kk + (1-kk) * act_gen + ans = np.clip(kb*history + kf * sine, -1, 1) + ans = (ans + 1) /2 # 100 * 12 + ans = lerp_np(low_np, upp_np, ans) + + return ans, history +def ang_trans(lower, upper, x): + """ + trans the rate x to the angle + """ + if isinstance(lower, list): + return [ang_trans(lower[i], upper[i], x[i]) for i in range(len(lower))] + else: + x = (x+1)/2 #todo for test + return x * upper + (1-x) * lower +def norm(lower, upper, x): + # print(lower, upper, x) + # assert abs((x - lower) / (upper - lower) ) <= 1, f"{abs((x - lower) / (upper - lower) )} {x, upper, lower} " + return (x - lower) / (upper - lower) +def deg_rad(x): + if isinstance(x, list): + return [deg_rad(a) for a in x] + else: + return x / 180 * pi + +def rad_deg(x): + if isinstance(x, list): + return [rad_deg(a) for a in x] + else: + return x / pi * 180 +def deg_normalize(lower, upper, x): + """ + make angle to rate from -1 -- 1 + """ + if isinstance(lower, list): + ans = [] + for a,b,c in zip(lower, upper, x): + ans.append(deg_normalize(a,b,c)) + return ans + else: + return 2 * norm(lower, upper, x) - 1 #todo for test + # return 1 - 2* norm(lower, upper,x) +low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] +upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] +tha1,tha2 = 30, 30 +u0 = [0, tha2, -2*tha2, 0 ,tha1, -tha1 * 2, 0 , tha2, -2*tha2, 0, tha1,-2*tha1] + +low_np = np.array(low) +upp_np = np.array(upp) +u0 = deg_normalize(low, upp, u0) +u0_ang = ang_trans(low, upp, u0) + +def sine_gene_pt(idx, T, rate): + # print(idx) + if isinstance(idx, np.ndarray) or isinstance(idx, list): + mat = np.zeros((len(idx), 12)) + i_set = set(idx) + idd = {} + for i in i_set: + idd[i] = sine_gene_pt(i, T, rate) + for i in range(len(idx)): + mat[i] = idd[idx[i]] + return mat + if not isinstance(idx, list): + angle_list = [0 for i in range(12)] + if idx >= 2 * T: + idx = 0 + dh = rate #:w + + + if idx >= 0 and idx <= T: + tp0 =idx - 0 + y1 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y2 = 0 + # y2 = y1 + elif idx>T and idx<=2*T: + tp0 =idx - T + y2 = dh * 10 * (-cos(pi * 2 * tp0 / T) + 1 ) / 2 + y1 = 0 + # y1 = y2 + # idx = idx % T + # y1 = dh * 10 * (-cos(pi * 2 *idx /T) + 1) + # y2 = deg_rad(60) + angle_list[0] = 0 + angle_list[1] = y1 + angle_list[2] = -2 * y1 + angle_list[3] = 0 + angle_list[4] = y2 + angle_list[5] = -2 * y2 + angle_list[6] = 0 + angle_list[7] = y2 + angle_list[8] = -2 * y2 + angle_list[9] = 0 + angle_list[10] = y1 + angle_list[11] = -2 * y1 + + angle_list = [deg_normalize(low[i], upp[i], angle_list[i] + u0_ang[i]) for i in range(12)] + + return angle_list + else: + ans = [] + for i in idx: + ans.append(sine_gene_pt(i, T, rate)) + return ans +def list_pt(act_gen, idx, T): + ans = [] + for i in range(act_gen.shape[0]): + ans.append(run_model_with_pt_input(act_gen[i, :], idx[i], T)) + ans = np.vstack(ans).astype(np.float32) + return ans +def run_model_with_pt_input_modify(act_gen, idx, T, history, kb, rate): + # act_gen = np.clip(act_gen- 1, -1, 1) + if isinstance(idx, list): + idx = np.array(idx) + idx = idx%(2*T) + else: + idx = idx % (2 * T) + # act_gen = np.zeros_like(act_gen) + sine = sine_gene_pt(idx, T, rate) + ans, history = add_list_np(act_gen, sine, history, kb) + ans = ans / 180 * 3.14 + + return ans.astype(np.float32), history.astype(np.float32) + +if __name__=='__main__': + print(run_model_with_pt_input_modify(np.zeros((1,12)), 1, 50, np.zeros((1,12)))) \ No newline at end of file From 20a73bd2f8dd0977682c88d631a577cd138d1876 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Mon, 21 Aug 2023 15:23:25 +0800 Subject: [PATCH 33/62] this version is able to wheel with real time in raisim --- raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py index 0d420bbca..324ba3f1d 100644 --- a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py @@ -19,7 +19,7 @@ def ang_trans(lower, upper, x): if isinstance(lower, list): return [ang_trans(lower[i], upper[i], x[i]) for i in range(len(lower))] else: - x = (x+1)/2 #todo for test + x = (x+1)/2 return x * upper + (1-x) * lower def norm(lower, upper, x): # print(lower, upper, x) @@ -46,7 +46,7 @@ def deg_normalize(lower, upper, x): ans.append(deg_normalize(a,b,c)) return ans else: - return 2 * norm(lower, upper, x) - 1 #todo for test + return 2 * norm(lower, upper, x) - 1 # return 1 - 2* norm(lower, upper,x) low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] From 96ede5e41877f11deb2a9791c21453147e0b051a Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Mon, 21 Aug 2023 22:22:03 +0800 Subject: [PATCH 34/62] able to turn not able to walk --- .../deploy_utils/runner_util.py | 13 +++---- .../env/VectorizedEnvironment.hpp | 2 +- .../env/envs/rsg/Environment_wheel.hpp | 22 +++++++----- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 12 +++---- .../raisimGymTorch/env/envs/rsg/fake_a1.py | 34 +++++++++++++++++++ .../raisimGymTorch/env/envs/rsg/on_p_train.py | 13 +++---- 6 files changed, 67 insertions(+), 29 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/env/envs/rsg/fake_a1.py diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py index 324ba3f1d..519cf15e0 100644 --- a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py @@ -3,9 +3,9 @@ def lerp_np(a, b, c): return a* (1-c) + b*c def add_list_np(act_gen, sine, history,kb): - kk = 0.9 + kk = 0.95 kf = 1 - kb = kb + kb = np.array([0.2, 0.1, 0.1] * 4) history = history*kk + (1-kk) * act_gen ans = np.clip(kb*history + kf * sine, -1, 1) ans = (ans + 1) /2 # 100 * 12 @@ -50,14 +50,15 @@ def deg_normalize(lower, upper, x): # return 1 - 2* norm(lower, upper,x) low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] -tha1,tha2 = 30, 30 -u0 = [0, tha2, -2*tha2, 0 ,tha1, -tha1 * 2, 0 , tha2, -2*tha2, 0, tha1,-2*tha1] +tha1,tha2 = 35,35 +u0 = [0, tha2, -2*tha2 + 10, 0 ,tha1, -tha1 * 2 +10 , 0 , tha2, -2*tha2 + 10, 0, tha1,-2*tha1 + 10] low_np = np.array(low) upp_np = np.array(upp) +u0_rad = deg_rad(u0) + u0 = deg_normalize(low, upp, u0) u0_ang = ang_trans(low, upp, u0) - def sine_gene_pt(idx, T, rate): # print(idx) if isinstance(idx, np.ndarray) or isinstance(idx, list): @@ -131,4 +132,4 @@ def run_model_with_pt_input_modify(act_gen, idx, T, history, kb, rate): return ans.astype(np.float32), history.astype(np.float32) if __name__=='__main__': - print(run_model_with_pt_input_modify(np.zeros((1,12)), 1, 50, np.zeros((1,12)))) \ No newline at end of file + print(run_model_with_pt_input_modify(np.zeros((1,12)), 1, 50, np.zeros((1,12)), 0.15, 0.6)) \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp b/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp index 627f468c6..8e4583230 100755 --- a/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp +++ b/raisimGymTorch/raisimGymTorch/env/VectorizedEnvironment.hpp @@ -237,7 +237,7 @@ class NormalSampler { } void seed(int seed) { - // this ensures that every thread gets a different seed + // this ensures that every thread gets a different see #pragma omp parallel for schedule(static, 1) for (int i = 0; i < THREAD_COUNT; i++) normal_[0].seed(i + seed); diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp index 5f5c749f8..2ffe1f3bc 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp @@ -78,12 +78,12 @@ namespace raisim { // skate_vel_.setZero(8); // skate_posi_.setZero(9); // init_position(); -double aa = 0.5233 , bb = double(30) /180*PI; - gc_init_<< 0, 0, 0.33, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa,-2*aa, 0.0, bb, -2*bb; +double aa = double(35)/ 180 * PI , bb = double(35) /180*PI; + gc_init_<< 0, 0, cos(aa) * 2 * 0.2, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa,-2*aa, 0.0, bb, -2*bb; // gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; init(); - obDim_ = 29; + obDim_ = 31; actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); obDouble_.setZero(obDim_); @@ -188,13 +188,13 @@ double aa = 0.5233 , bb = double(30) /180*PI; } updateObservation(); double rrr =0; - rrr = abs(euler_angle[0]) + abs(euler_angle[1]) ; - rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1])); + rrr = abs(euler_angle[0]) + abs(euler_angle[1]) + abs(euler_angle[2]) ; + rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1]) + abs(ang_vel_[2])); // rrr += abs(gc_[0] - skate_posi_[0]) + abs(gc_[1] - (skate_posi_[1] - 0.15)); bool accu = false; rewards_.record("Stable",-rrr, accu); rewards_.record("Live", 1, accu); -// rewards_.record("forwardVel", skate_vel_[0], accu); + rewards_.record("forwardVel", -abs(3- line_vel_[0]), accu); // rewards_.record("height", 0.45- abs(gc_[2] - 0.45) - abs(gc_[0] ) - abs(gc_[1]) , accu); // rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); @@ -204,6 +204,9 @@ double aa = 0.5233 , bb = double(30) /180*PI; } void updateObservation() { +// raisim::Vec<3> acc_w; + +// std::cout<< "acc? " <getFrameAcceleration("base",acc_w); anymal_->getState(gc_, gv_); // skate -> getState(skate_posi_, skate_vel_); @@ -212,9 +215,10 @@ double aa = 0.5233 , bb = double(30) /180*PI; quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; quat /= quat.norm(); raisim::quatToRotMat(quat, rot); - bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); +// bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); anymal_->getAngularVelocity(anymal_->getBodyIdx("base"), ang_vel_); + anymal_->getFrameVelocity(anymal_->getBodyIdx("base"), line_vel_); Eigen::Quaterniond qua(gc_[3], gc_[4], gc_[5], gc_[6]); euler_angle = ToEulerAngles(qua); Eigen::VectorXd gcc = gc_.tail(12); @@ -233,6 +237,8 @@ double aa = 0.5233 , bb = double(30) /180*PI; ang_vel_[0], ang_vel_[1], ang_vel_[2], + line_vel_[0], + line_vel_[1], c_v; } @@ -292,7 +298,7 @@ double aa = 0.5233 , bb = double(30) /180*PI; Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; double terminalRewardCoeff_ = -10.; Eigen::VectorXd actionMean_, actionStd_, obDouble_; - Vec<3> acc_, ang_vel_; + Vec<3> acc_, ang_vel_, line_vel_; Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; std::set footIndices_; double p_gain,d_gain; diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 0740969c9..72996ee48 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -4,9 +4,9 @@ record_video: true environment: render: true # just testing commenting - num_envs: 1 + num_envs: 100 eval_every_n: 40 - num_threads: 1 + num_threads: 30 simulation_dt: 0.0025 control_dt: 0.01 max_time: 4 @@ -23,7 +23,7 @@ environment: urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: forwardVel: - coeff: 0. + coeff: 0.3 torque: coeff: -4e-5 Stable: @@ -33,7 +33,7 @@ environment: Live: coeff: 1 Wheel: - coeff: 0.3 + coeff: 0 # Mimic: # coeff: 0. @@ -42,5 +42,5 @@ architecture: value_net: [128, 128] on_policy: - rate: 0.6 # for sine A - kb: 0.05 # act_gen * kb + rate: 0.8 # for sine A + kb: 0.15 # act_gen * kb diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/fake_a1.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/fake_a1.py new file mode 100644 index 000000000..cc35b8a8f --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/fake_a1.py @@ -0,0 +1,34 @@ +import numpy as np +class Robot: + def __init__(self): + self.ob_dims = 29 + self.act_dims = 12 + self.position = [0, 0.5233, -1.0466] * 4 + + + + def observe(self): + return np.array( + [[0. , 0. , 0. , 0. , 0. , 0., + 0. , 0.5233 , 0., - 1.0466 , 0. , 0., + 0. , 0.5235988, 0., - 1.0471976 , 0. , 0., + 0. , 0.5233 , 0., - 1.0466 , 0. , 0., + 0. , 0.5235988, 0., - 1.0471976 , 0.]] + ).astype(np.float32) + + def ob_dim(self): + return self.ob_dims + + def act_dim(self): + return self.act_dims + + def take_action(self,action): + pass + + def init_motor(self,act): + pass + +def init_position(a,b): + pass + +a1 = Robot() \ No newline at end of file diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py index c24201e36..09e6dfb69 100755 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/on_p_train.py @@ -67,7 +67,7 @@ total_steps = n_steps * env.num_envs avg_rewards = [] - +print(env.num_envs) actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, env.num_envs, @@ -82,8 +82,6 @@ save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) logger = RaisimLogger(saver.data_dir+"/train.log") - - def updating(): obs = env.observe(False) ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) @@ -102,14 +100,11 @@ def updating(): env.save_scaling(saver.data_dir, str(update)) save_act.save() save_observe.save() - - avg_rewards.append(average_ll_performance) actor.update() actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) - print('----------------------------------------------------') print('{:>6}th iteration'.format(update)) print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) @@ -146,9 +141,9 @@ def updating(): on_p_rate =cfg['on_policy']['rate'] on_p_kb = cfg['on_policy']['kb'] - mode == 'train' - +his_util = [0] * 12 +history_act = np.array([his_util] * num_envs) total_update = args.update if mode =='train' or mode == 'retrain': env.turn_on_visualization() @@ -162,7 +157,9 @@ def updating(): for step in range(n_steps): waiter.wait() obs = env.observe(False) + # print(obs) action = ppo.act(obs) + # action = np.zeros_like(action) action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act, kb=on_p_kb, rate=on_p_rate) reward, _ = env.step(action) From 336d561df78f3a13f2db7c2785391b5448edf8a8 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 22 Aug 2023 18:16:06 +0800 Subject: [PATCH 35/62] inverse turning with this version --- .../deploy_utils/runner_util.py | 8 +- .../env/envs/rsg/Environment_wheel.hpp | 47 +-- .../raisimGymTorch/env/envs/rsg/cfg.yaml | 14 +- .../env/envs/rsg/real_on_policy_train.py | 268 ++++++++++++++++++ 4 files changed, 304 insertions(+), 33 deletions(-) create mode 100755 raisimGymTorch/raisimGymTorch/env/envs/rsg/real_on_policy_train.py diff --git a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py index 519cf15e0..80737271f 100644 --- a/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py +++ b/raisimGymTorch/raisimGymTorch/deploy_utils/runner_util.py @@ -5,7 +5,8 @@ def lerp_np(a, b, c): def add_list_np(act_gen, sine, history,kb): kk = 0.95 kf = 1 - kb = np.array([0.2, 0.1, 0.1] * 4) + # kb = np.array([0.2 ,0.1, 0.1] * 4) + kb = np.array([0.3, 0., 0.] * 4) history = history*kk + (1-kk) * act_gen ans = np.clip(kb*history + kf * sine, -1, 1) ans = (ans + 1) /2 # 100 * 12 @@ -50,8 +51,9 @@ def deg_normalize(lower, upper, x): # return 1 - 2* norm(lower, upper,x) low = [-46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5, -46, -60, -154.5] upp = [46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5, 46, 240, -52.5] -tha1,tha2 = 35,35 -u0 = [0, tha2, -2*tha2 + 10, 0 ,tha1, -tha1 * 2 +10 , 0 , tha2, -2*tha2 + 10, 0, tha1,-2*tha1 + 10] +tha1,tha2 = 40, 40 +for_r = -2.5 +u0 = [0, tha2, -2*tha2 , 0 ,tha1, -tha1 * 2 , 0 , tha2 + for_r , -2*tha2 - 2 * for_r , 0, tha1 + for_r ,-2*tha1 -2 * for_r] low_np = np.array(low) upp_np = np.array(upp) diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp index 2ffe1f3bc..c7a1eee69 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/Environment_wheel.hpp @@ -57,8 +57,8 @@ namespace raisim { // skate = world_ ->addArticulatedSystem("/home/lr-2002/code/raisimLib/rsc/skate/skate.urdf"); anymal_->setName("model"); anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); - world_->addGround(0,"land"); - +// world_->addGround(0,"land"); +world_->addGround(); /// get robot data gcDim_ = anymal_->getGeneralizedCoordinateDim(); // 19 gvDim_ = anymal_->getDOF(); // 18 @@ -78,8 +78,9 @@ namespace raisim { // skate_vel_.setZero(8); // skate_posi_.setZero(9); // init_position(); -double aa = double(35)/ 180 * PI , bb = double(35) /180*PI; - gc_init_<< 0, 0, cos(aa) * 2 * 0.2, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa,-2*aa, 0.0, bb, -2*bb; +double aa = double(40)/ 180 * PI , bb = double(40) /180*PI; + double for_r = double(-2.5) / 180 * PI ; + gc_init_<< 0, 0, cos(aa) * 2 * 0.2, 1.0, 0.0, 0.0, 0.0, 0.0, aa, -2*aa, 0.0, bb, -2*bb, 0.0,aa +for_r ,-2*aa -2 * for_r , 0.0, bb + for_r, -2*bb -2 * for_r; // gc_init_<< 0, 0, 0.37, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5233, -1.046, 0.0, 0.5233, -1.046, 0.0, 0.523, -1.046, 0.0, 0.523, -1.046; init(); @@ -99,24 +100,24 @@ double aa = double(35)/ 180 * PI , bb = double(35) /180*PI; footIndices_.insert(anymal_->getBodyIdx("RL_calf")); footIndices_.insert(anymal_->getBodyIdx("RR_calf")); - for(auto n : anymal_->getCollisionBodies()) - { - auto name = n.colObj->name; - anymal_->getCollisionBody(name).setMaterial("steel"); - } +// for(auto n : anymal_->getCollisionBodies()) +// { +// auto name = n.colObj->name; +// anymal_->getCollisionBody(name).setMaterial("steel"); +// } // skate->getCollisionBody("base/0").setMaterial("sandpaper"); // skate->getCollisionBody("rotater_r/0").setMaterial("rubber"); // skate->getCollisionBody("rotater_f/0").setMaterial("rubber"); - anymal_->getCollisionBody("FL_foot/0").setMaterial("rubber"); - anymal_->getCollisionBody("FR_foot/0").setMaterial("rubber"); - anymal_->getCollisionBody("RR_foot/0").setMaterial("rubber"); - anymal_->getCollisionBody("RL_foot/0").setMaterial("rubber"); - world_->setMaterialPairProp("steel", "rubber", 0.8, 0.15, 0.001); - world_->setMaterialPairProp("rubber", "sandpaper", 0.99, 0.15, 0.001); - world_->setMaterialPairProp("land", "rubber", 0.8, 0.1,0.001); - world_->setMaterialPairProp("sandpaper", "land", 0.4, 0.15,0.001); - world_->setMaterialPairProp("steel","land", 0.1, 0.05,0.001); +// anymal_->getCollisionBody("FL_foot/0").setMaterial("rubber"); +// anymal_->getCollisionBody("FR_foot/0").setMaterial("rubber"); +// anymal_->getCollisionBody("RR_foot/0").setMaterial("rubber"); +// anymal_->getCollisionBody("RL_foot/0").setMaterial("rubber"); +// world_->setMaterialPairProp("steel", "rubber", 0.8, 0.15, 0.001); +// world_->setMaterialPairProp("rubber", "sandpaper", 0.99, 0.15, 0.001); +// world_->setMaterialPairProp("land", "rubber", 0.8, 0.1,0.001); +// world_->setMaterialPairProp("sandpaper", "land", 0.4, 0.15,0.001); +// world_->setMaterialPairProp("steel","land", 0.1, 0.05,0.001); if (visualizable_) { server_ = std::make_unique(world_.get()); @@ -188,15 +189,15 @@ double aa = double(35)/ 180 * PI , bb = double(35) /180*PI; } updateObservation(); double rrr =0; - rrr = abs(euler_angle[0]) + abs(euler_angle[1]) + abs(euler_angle[2]) ; - rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1]) + abs(ang_vel_[2])); + rrr = abs(euler_angle[0]) + abs(euler_angle[1]) ; + rrr += 0.1 * ( abs(ang_vel_[0]) + abs(ang_vel_[1])); // rrr += abs(gc_[0] - skate_posi_[0]) + abs(gc_[1] - (skate_posi_[1] - 0.15)); bool accu = false; rewards_.record("Stable",-rrr, accu); rewards_.record("Live", 1, accu); rewards_.record("forwardVel", -abs(3- line_vel_[0]), accu); // rewards_.record("height", 0.45- abs(gc_[2] - 0.45) - abs(gc_[0] ) - abs(gc_[1]) , accu); -// rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); + rewards_.record("Mimic", (gc_.tail(12) - pTarget12_).norm(), accu); // rewards_.record("Wheel", euler_angle[2] * double(COUNT) / 400, accu); rewards_.record("Wheel", 0.5 - abs(ang_vel_[2]- 0.5), accu); rewards_.record("torque",anymal_->getGeneralizedForce().squaredNorm() ); @@ -237,8 +238,8 @@ double aa = double(35)/ 180 * PI , bb = double(35) /180*PI; ang_vel_[0], ang_vel_[1], ang_vel_[2], - line_vel_[0], - line_vel_[1], +// line_vel_[0], +// line_vel_[1], c_v; } diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml index 72996ee48..d2cfab356 100644 --- a/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/cfg.yaml @@ -4,9 +4,9 @@ record_video: true environment: render: true # just testing commenting - num_envs: 100 + num_envs: 1 eval_every_n: 40 - num_threads: 30 + num_threads: 1 simulation_dt: 0.0025 control_dt: 0.01 max_time: 4 @@ -23,19 +23,19 @@ environment: urdf_path: /home/lr-2002/code/raisimLib/rsc/a1_description/urdf/a1.urdf reward: forwardVel: - coeff: 0.3 + coeff: 0. torque: coeff: -4e-5 Stable: - coeff: 0.4 + coeff: 0. height: coeff: 0. Live: coeff: 1 Wheel: - coeff: 0 -# Mimic: -# coeff: 0. + coeff: 2 + Mimic: + coeff: 0. architecture: policy_net: [128, 128] diff --git a/raisimGymTorch/raisimGymTorch/env/envs/rsg/real_on_policy_train.py b/raisimGymTorch/raisimGymTorch/env/envs/rsg/real_on_policy_train.py new file mode 100755 index 000000000..9b15f748d --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/env/envs/rsg/real_on_policy_train.py @@ -0,0 +1,268 @@ +import concurrent.futures +import threading + +import asn1crypto.cms +import asn1crypto.core +from raisimGymTorch.env.bin.rsg import NormalSampler +from ruamel.yaml import YAML, dump, RoundTripDumper +from raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver, load_param, tensorboard_launcher, RaisimLogger +import raisimGymTorch.algo.ppo.module as ppo_module +import raisimGymTorch.algo.ppo.ppo as PPO +from raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv +from raisimGymTorch.env.bin.rsg import RaisimGymEnv +from raisimGymTorch.deploy_utils.angle_utils import get_last_position +from raisimGymTorch.deploy_utils.runner_util import run_model_with_pt_input_modify, list_pt, u0_rad +import os +import math +import time +import torch.nn as nn +import numpy as np +import torch +import datetime +import argparse +import signal +from raisimGymTorch.deploy_log.draw_map import Drawer +from raisimGymTorch.deploy_log.csv_saver import CSV_saver +# task specification +# from fake_a1 import * +from robot_utils import * + +task_name = "real_time_train_test" + +# configuration +parser = argparse.ArgumentParser() +parser.add_argument('-m', '--mode', help='set mode either train or test', type=str, default='train') +parser.add_argument('-w', '--weight', help='pre-trained weight path', type=str, default='') +parser.add_argument('-u', '--update', help='update times', type=int, default=300) +parser.add_argument('-p', '--cfg_path', help='where to find the path', type=str, default=None) +# parser.add_argument('-b', '--load_best', help='load best file in last train', type=bool, default=False) +args = parser.parse_args() +mode = args.mode +weight_path = args.weight +cfg_path = args.cfg_path +# load_best = args.load_best +# check if gpu is available +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +print(device) +# directories +task_path = os.path.dirname(os.path.realpath(__file__)) +home_path = task_path + "/../../../../.." + +# config +if cfg_path is not None: + cfg = YAML().load(open(cfg_path, 'r')) +else: + cfg = YAML().load(open(task_path + "/cfg.yaml", 'r')) + +# shortcuts + + +env = VecEnv(RaisimGymEnv(home_path + "/rsc", dump(cfg['environment'], Dumper=RoundTripDumper))) +env.seed(cfg['seed']) + +ob_dim = a1.ob_dim() +act_dim = a1.act_dim() + +# Training +n_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt']) +total_steps = n_steps * 1 + +avg_rewards = [] +num_envs=1 +actor = ppo_module.Actor(ppo_module.MLP(cfg['architecture']['policy_net'], nn.LeakyReLU, ob_dim, act_dim), + ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, + num_envs, + 1.0, + NormalSampler(act_dim), + cfg['seed']), + device) +critic = ppo_module.Critic(ppo_module.MLP(cfg['architecture']['value_net'], nn.LeakyReLU, ob_dim, 1), + device) + +saver = ConfigurationSaver(log_dir=home_path + "/raisimGymTorch/data/"+task_name, + save_items=[task_path + "/cfg.yaml", task_path + "/Environment.hpp"]) +logger = RaisimLogger(saver.data_dir+"/train.log") + +def real_handler(signal, frame): + print('You pressed Ctrl+C!') + a1.quit_robot() + # debug_draw_gry.draw() + draw_reward.draw() + save_reward.save() + sys.exit(0) + +signal.signal(signal.SIGINT, real_handler) + +init_e_x = 0 +init_e_y = 0 +# init_v_x = 0 +# init_v_y = 0 +def cal_reward(robot): + """ + + :param robot: a1 + :return: reward number + """ + global init_e_x + global init_e_y + # global init_v_x + # global init_v_y + obs = robot.observe()[0] + reward = 0 + # reward -= (abs(obs[0] - init_e_x )+ abs(obs[1] - init_e_y)) + # reward -= (abs(obs[2]) + abs(obs[3])) * 0.1 + # reward = reward * 0.4#stable + # reward += 1 + # reward = reward + (0.5 - abs(obs[4] + 0.5)) * 2 # wheel + reward = reward + obs[4]# wheel + + return np.array([reward]) + +def updating(): + obs = a1.observe() + ppo.update(actor_obs=obs, value_obs=obs, log_this_iteration=update % 2 == 0, update=update) + average_ll_performance = reward_sum / total_steps + + + if update % cfg['environment']['eval_every_n'] == 0: + biggest_iter = update + biggest_reward = average_ll_performance + torch.save({ + 'actor_architecture_state_dict': actor.architecture.state_dict(), + 'actor_distribution_state_dict': actor.distribution.state_dict(), + 'critic_architecture_state_dict': critic.architecture.state_dict(), + 'optimizer_state_dict': ppo.optimizer.state_dict(), + }, saver.data_dir + "/full_" + str(update) + '.pt') + save_act.save() + save_observe.save() + + # draw_reward.draw() + + avg_rewards.append(average_ll_performance) + draw_reward.add_map_list([average_ll_performance]) + save_reward.add_list([average_ll_performance]) + actor.update() + actor.distribution.enforce_minimum_std((torch.ones(12)).to(device)) + + print('----------------------------------------------------') + print('{:>6}th iteration'.format(update)) + print('{:<40} {:>6}'.format("average ll reward: ", '{:0.10f}'.format(average_ll_performance))) + print('----------------------------------------------------\n') + + + +num_envs = cfg['environment']['num_envs'] +ppo = PPO.PPO(actor=actor, + critic=critic, + num_envs=cfg['environment']['num_envs'], + num_transitions_per_env=n_steps, + num_learning_epochs=4, + gamma=0.996, + lam=0.95, + num_mini_batches=16, + device=device, + log_dir=saver.data_dir, + ) + + +biggest_reward = 0 +biggest_iter = 0 + +save_observe = CSV_saver('./observation_saver') +save_act = CSV_saver('./act_saver') +save_reward = CSV_saver('./reward_saver') +draw_reward = Drawer('./draw_reward') +print = logger.info + + + +on_p_rate =cfg['on_policy']['rate'] +on_p_kb = cfg['on_policy']['kb'] +mode == 'train' +his_util = [0] * 12 +history_act = np.array([his_util] * num_envs) +total_update = args.update + +obs = a1.observe() +a1.torque_limit =31 +ppo.act(obs) +act = a1.position +a1.kp=[150] * 12 +a1.kd = [7] * 12 +envs_idx = 0 +schedule = cfg['environment']['schedule'] + +action, _ = run_model_with_pt_input_modify(np.zeros((1,12)), 0, schedule, history_act, kb=on_p_kb, + rate=on_p_rate) + +a1.init_motor(act) +init_position(action[0].tolist(), 250) +a1.observe() +print(f'actural position {a1.position}') +for i in range(500): + if i >30 and i <=40: + obs = a1.observe() + init_e_x += obs[0][0] + init_e_y += obs[0][1] + if i==40: + init_e_y /= 10 + init_e_x /= 10 + a1.hold_on() + # print(a1.position) +# obs = a1.observe() + +print(f'actual zero angle : {init_e_x, init_e_y}') + +# obb = obs.copy() +# ppo_act = threading.Thread(target=ppo.act(obs)) +# ppo_act.start() +# while ppo_act.is_alive(): +# a1.hold_on() +# print('holding') +# print('finished hold /on ') +envs_idx = 0 +for update in range(total_update): + reward_sum = 0 + if update==0: + a1.hold_on() + # print('1') + for step in range(n_steps): + # waiter.wait() + obs = a1.observe() + # print(3) + action = ppo.act(obs) + if update == 0 and step == 0 : + a1.hold_on() + # print('2') + + # action = np.zeros_like(action) + + # action=np.clip(action-1, -1, 1) + # print(4) + action, history_act = run_model_with_pt_input_modify(action, envs_idx, schedule, history_act, kb=on_p_kb, rate=on_p_rate) + action=action[0] + if step==0 and update ==0: + print(f"pre take action the first time ") + # print('take action ') + a1.take_action(action.tolist()) + reward = cal_reward(a1) + envs_idx +=1 + + ppo.step(value_obs=obs, rews=reward, dones=np.array([False])) + + save_observe.add_list(obs[0]) + save_act.add_list(action[0]) + + reward_sum = reward_sum + np.sum(reward) + + # take st step to get value obs + update_thread = threading.Thread(target=updating) + update_thread.start() + while update_thread.is_alive(): + # waiter.wait() + print('threading running') + a1.take_action(action.tolist()) + print('updating finished') +print(f'biggest:{biggest_reward},rate = {biggest_iter}') + + From 34e041a6cddc010676740a00e9a0737b3e52da06 Mon Sep 17 00:00:00 2001 From: Lr-2002 <2629651228@qq.com> Date: Tue, 22 Aug 2023 18:53:02 +0800 Subject: [PATCH 36/62] inverse turning data here --- .../raisimGymTorch/deploy_log/anay.py | 28 +- .../trained_data/inverse_turn/Environment.hpp | 466 + .../trained_data/inverse_turn/full_0.pt | Bin 0 -> 531091 bytes .../trained_data/inverse_turn/full_120.pt | Bin 0 -> 533119 bytes .../trained_data/inverse_turn/full_40.pt | Bin 0 -> 532809 bytes .../trained_data/inverse_turn/full_80.pt | Bin 0 -> 532809 bytes .../trained_data/inverse_turn/reward.csv | 134 + .../trained_data/inverse_turn/train.log | 8400 +++++++++++++++++ 8 files changed, 9017 insertions(+), 11 deletions(-) create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/Environment.hpp create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/full_0.pt create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/full_120.pt create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/full_40.pt create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/full_80.pt create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/reward.csv create mode 100644 raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/train.log diff --git a/raisimGymTorch/raisimGymTorch/deploy_log/anay.py b/raisimGymTorch/raisimGymTorch/deploy_log/anay.py index 5f5d574eb..5410e8140 100644 --- a/raisimGymTorch/raisimGymTorch/deploy_log/anay.py +++ b/raisimGymTorch/raisimGymTorch/deploy_log/anay.py @@ -1,14 +1,20 @@ -# import re -# with open("./08-11-15-28", 'r') as f: -# lines = f.readlines() -# for line in lines: -# # x = re.search("work_action", line) -# # print(x) -# if "work_action" in line: -# print(line) - - +import re import pandas as pd +ans_list = [] import numpy as np -import matplotlib.pyplot as plt +with open("./train.log", 'r') as f: + lines = f.readlines() + for line in lines: + # x = re.search("work_action", line) + # print(x) + if "average" in line: + line = line.strip() + # print(line[-20:]) + x = float(line[-20:]) + ans_list.append(x) + + +df = np.array(ans_list) +dd = pd.DataFrame(df).to_csv('./tss.csv') + diff --git a/raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/Environment.hpp b/raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/Environment.hpp new file mode 100644 index 000000000..82a196374 --- /dev/null +++ b/raisimGymTorch/raisimGymTorch/trained_data/inverse_turn/Environment.hpp @@ -0,0 +1,466 @@ +//----------------------------// +// This file is part of RaiSim// +// Copyright 2020, RaiSim Tech// +//----------------------------// + +#pragma once + +#include +#include +//#include "yaml.h" +#include "../../RaisimGymEnv.hpp" +#define PI 3.1415926 + +double cal(double low, double upp, double now){ + return (now + 1)/2 * (upp- low) + low; +} +void swap(Eigen::VectorXd &ob, int a, int b) +{ + double tmp = ob[a]; + ob[a] = ob[b]; + ob[b] = ob[a]; +} + +double rad_deg(double rad) +{ + return rad / PI * 180; +} + +void map_from_origin_to_limit(std::vector> join, Eigen::VectorXd& limit_list, Eigen::VectorXd& gen_list) +{ +// Eigen::VectorXd limit_list; +// join = join.tail(12); + limit_list.setZero(join.size() - 6); +// std::cout<< "!------------" << gen_list.size()<< std::endl; + int cnt = 0; + int jp = 6; + int kk = 0; + for( auto i : join) + { + if(kk < jp){ + kk++; + continue; + } +// std::cout << i[1] << " " << i[0]<< std::endl; + limit_list[cnt] = (cal(i[0] , i[1], gen_list[cnt])); + cnt++; + } +} + +//void angle_generator(Eigen::VectorXd& angle_list, int idx, float T, float rate=1.f) +//{ +//// std::cout<< "size is "<< angle_list.size() << std::endl; +// double base1= 0.82, base3=0.0; +// double base2 = -2 * base1; +// double ang = abs(sin( float(idx) / T * PI)) * rate; +// +// int idx_base = 0; +//// std::cout<(); + + READ_YAML(double, action_std, cfg_["action_std"]) /// example of reading params from the config + READ_YAML(bool, show_ref, cfg_["show_ref"]); + if (show_ref == 0) show_ref = false; + READ_YAML(double, angle_rate, cfg_["angle_rate"]); + READ_YAML(double, stable_reward_rate, cfg_["stable"]); + READ_YAML(double, reference_rate, cfg_["reference"]); + READ_YAML(double, for_work_rate, cfg_["for_work"]); + READ_YAML(bool, float_base, cfg_["float_base"]); + READ_YAML(float,schedule_T, cfg_["schedule"]); + /// add objects + anymal_ = world_->addArticulatedSystem(resourceDir_+"/a1_description/urdf/a1.urdf"); + anymal_->setName("dog"); + anymal_->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + world_->addGround(); + + /// get robot data + gcDim_ = anymal_->getGeneralizedCoordinateDim(); + gvDim_ = anymal_->getDOF(); + nJoints_ = gvDim_ - 6; + + /// initialize containers + gc_.setZero(gcDim_); gc_init_.setZero(gcDim_); + gv_.setZero(gvDim_); gv_init_.setZero(gvDim_); + pTarget_.setZero(gcDim_); vTarget_.setZero(gvDim_); pTarget12_.setZero(nJoints_); + angle_list.setZero(nJoints_);angle_list_for_work.setZero(nJoints_); + gc_old.setZero(nJoints_) ; ref_old.setZero(nJoints_); + /// this is nominal configuration of anymal +// gc_init_ << 0, 0, 0.50, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// gc_init_ << 3, 3, 0.54, 1.0, 0.0, 0.0, 0.0, 0.03, 0.4, -0.8, -0.03, 0.4, -0.8, 0.03, -0.4, 0.8, -0.03, -0.4, 0.8; +// anymal_1->setGeneralizedCoordinate(gc_init_); + gc_init_<< 0, 0, 0.41, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; + +// jointNominalConfig.tail(12).setZero(); +// Eigen::VectorXd tmp(12); +// tmp.setZero(); +// angle_generator(tmp, 0, 80.f); +// gc_init_.tail(12) = tmp; + + /// set pd gains + Eigen::VectorXd jointPgain(gvDim_), jointDgain(gvDim_); + jointPgain.setZero(); jointPgain.tail(nJoints_).setConstant(2000.0); + jointDgain.setZero(); jointDgain.tail(nJoints_).setConstant(100); + anymal_->setPdGains(jointPgain, jointDgain); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + anymal_->setGeneralizedForce(Eigen::VectorXd::Zero(gvDim_)); + + + if(show_ref) + { + std::cout<<"using ref"; + anymal_1 = world_->addArticulatedSystem(resourceDir_+"/anymal_c/urdf/anymal.urdf"); + anymal_1 ->setName("anymal_ref"); + anymal_1->setControlMode(raisim::ControlMode::PD_PLUS_FEEDFORWARD_TORQUE); + anymal_1->setPdGains(jointPgain,jointDgain); + } + + /// MUST BE DONE FOR ALL ENVIRONMENTS + obDim_ = 34; + actionDim_ = nJoints_; actionMean_.setZero(actionDim_); actionStd_.setZero(actionDim_); + obDouble_.setZero(obDim_); + + /// action scaling + actionMean_ = gc_init_.tail(nJoints_); + +// READ_YAML(); + actionStd_.setConstant(action_std); + + join_limit = anymal_->getJointLimits(); + /// Reward coefficients + rewards_.initializeFromConfigurationFile (cfg["reward"]); + + /// indices of links that should not make contact with ground + footIndices_.insert(anymal_->getBodyIdx("FL_calf")); + footIndices_.insert(anymal_->getBodyIdx("FR_calf")); + footIndices_.insert(anymal_->getBodyIdx("RL_calf")); + footIndices_.insert(anymal_->getBodyIdx("RR_calf")); + + /// visualize if it is the first environment + if (visualizable_) { + server_ = std::make_unique(world_.get()); + server_->launchServer(); + server_->focusOn(anymal_); + } + } + + void init() final { } + + void reset() final { + anymal_->setState(gc_init_, gv_init_); + rewards_.setZero(); + COUNT=0; + updateObservation(); + } + + float step(const Eigen::Ref& action) final { + /// action scaling +// COUNT ++; +// std::cout<setBasePos(po); + pTarget_.tail(nJoints_) = angle_list; + anymal_1->setPdTarget(pTarget_, vTarget_); + } + if (float_base) + { + + Eigen::Vector3d po(3, 3 ,10); + anymal_->setBasePos(po); + + +// raisim::Vec<4> quat; +// raisim::Mat<3,3> rot; +// quat[0] = 0; quat[1] = 0; quat[2] = 0; quat[3] = 0; +// raisim::quatToRotMat(quat, rot); +// anymal_->setBaseOrientation(rot); +// raisim::Vec<4> quat(4); +// quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; +// anymal_->setBaseOrientation(quat); +// pTarget_.tail(nJoints_) = angle_list; +// anymal_->setPdTarget(pTarget_, vTarget_); + } + // angle_list *= 0.3; +// angle_list.setZero(); +// Eigen::VectorXd idx1(8), idx2(4); +// idx1.setZero();idx2.setZero(); +// idx1<<1, 2, 4, 5, 7,8,10,11; +// idx2<<0, 3, 6, 9; + + pTarget12_ = action.cast(); +// Eigen::VectorXd ttmp =action.cast(); +// map_from_origin_to_limit(join_limit, pTarget12_,ttmp); + // pTarget12_ = pTarget12_.cwiseProduct(actionStd_); +// pTarget12_ += actionMean_; +// pTarget12_ = angle_list; + + +// angle_mulit(pTarget12_, idx1, action_std); +// angle_mulit(pTarget12_, idx2, action_std); +// +// +// angle_list_for_work = angle_list * for_work_rate; + + + + +// pTarget12_ = angle_list_for_work + pTarget12_; + + + +// pTarget12_[11] = pTarget12_[10] *-2; +// pTarget12_[8] = pTarget12_[7] * -2; +// pTarget12_[1] = -pTarget12_[10]; +// pTarget12_[2] = -pTarget12_[11]; +// pTarget12_[4] = -pTarget12_[7]; +// pTarget12_[5] = -pTarget12_[8]; +// pTarget12_[9] = -0.03; +// pTarget12_[0] = -pTarget12_[9]; +// pTarget12_[6] = 0.03; +// pTarget12_[3] = -pTarget12_[6]; + pTarget_.tail(nJoints_) = pTarget12_; + + anymal_->setPdTarget(pTarget_, vTarget_); + + for(int i=0; i< int(control_dt_ / simulation_dt_ + 1e-10); i++){ + if(server_) server_->lockVisualizationServerMutex(); + world_->integrate(); + if(server_) server_->unlockVisualizationServerMutex(); + } + + + + updateObservation(); + double rrr =0; + for(int i=4; i<=6 ; i++) + { + rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; + } +// rrr *= float(COUNT )/ schedule_T); +// for(int i=0; i<=1; i++) +// { +// rrr += abs(gc_[i] - gc_init_[i]) * stable_reward_rate ; +// } +// rrr += abs(gc_[5] - gc_init_[5]) * 2 * stable_reward_rate; +// if(COUNT != 0) +// { +// rrr += (gc_ - gc_init_).norm() * reference_rate *0.1; +// } + + +// rrr +=(gc_init_.head(7) - gc_.head(7)).norm() * stable_reward_rate; + rrr += (gc_.tail(12) - angle_list).norm() * reference_rate; +// rrr += abs(bodyLinearVel_[1]); +// rrr += abs(bodyLinearVel_[2]); +// rrr += abs(bodyLinearVel_[0]); + +// rrr += abs(gc_[2] - gc_init_[2]) ; + +// rewards_.record("torque", anymal_->getGeneralizedForce().squaredNorm()); +// rewards_.record("forwardVel", std::mi); + rewards_.record("Stable", 1 - rrr, false); + rewards_.record("forwardVel", bodyLinearVel_[0], false); + + gc_old = gc_.tail(12); + ref_old = angle_list; + return rewards_.sum(); + } + + void updateObservation() { + anymal_->getState(gc_, gv_); + raisim::Vec<4> quat; + raisim::Mat<3,3> rot; + quat[0] = gc_[3]; quat[1] = gc_[4]; quat[2] = gc_[5]; quat[3] = gc_[6]; + raisim::quatToRotMat(quat, rot); + bodyLinearVel_ = rot.e().transpose() * gv_.segment(0, 3); + bodyAngularVel_ = rot.e().transpose() * gv_.segment(3, 3); +// std::cout<< rot.e().row(2).transpose().size() << " " < ob) final { + /// convert it to float + ob = obDouble_.cast(); + } + + bool isTerminalState(float& terminalReward) final { + terminalReward = float(terminalRewardCoeff_); + +// / if the contact body is not feet + for(auto& contact: anymal_->getContacts()) + if(footIndices_.find(contact.getlocalBodyIndex()) == footIndices_.end()) + {// if there is any contact body was not in the footIndices the over +// std::cout<<"terminate "<0.3 ) +// { +// return true; +// } +// if(abs(gc_[4]-gc_init_[4]) >0.2 ) +// { +// return true; +// } +// if(abs(gc_[6]-gc_init_[6]) >0.2 ) +// { +// return true; +// } +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8) +// return true; + +// if(abs(gc_[9]) > 1.8 || abs(gc_[12]) > 1.8 || abs(gc_[15]) > 1.8 || abs(gc_[18]) > 1.8 || ) +// return true; + + terminalReward = 0.f; + return false; + } + + void curriculumUpdate() { }; + + private: + int gcDim_, gvDim_, nJoints_; + bool visualizable_ = false; + raisim::ArticulatedSystem* anymal_, *anymal_1; + Eigen::VectorXd gc_init_, gv_init_, gc_, gv_, pTarget_, pTarget12_, vTarget_; + double terminalRewardCoeff_ = -10.; + Eigen::VectorXd actionMean_, actionStd_, obDouble_; + Eigen::Vector3d bodyLinearVel_, bodyAngularVel_; + std::set footIndices_; + std::vector> join_limit; + int COUNT = 0; + float schedule_T; + bool show_ref= true; + double action_std, angle_rate, reference_rate, stable_reward_rate,for_work_rate; + Eigen::VectorXd angle_list, angle_list_for_work; + Eigen::VectorXd gc_old, ref_old; + /// these variables are not in use. 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