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MuJoCo
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NVIDIA GR00T
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LeRobot
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Strands Docs
diff --git a/examples/01_sim_quickstart.py b/examples/01_sim_quickstart.py
new file mode 100644
index 0000000..342b04b
--- /dev/null
+++ b/examples/01_sim_quickstart.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""Quickstart: 5 lines from import to simulation.
+
+The Robot() factory auto-detects mode (defaults to sim when no hardware
+is connected) and returns a ready-to-use MuJoCo simulation.
+
+Requirements:
+ pip install strands-robots[sim]
+
+Usage:
+ python examples/01_sim_quickstart.py
+"""
+
+from strands_robots import Robot
+
+# Create a simulated SO-100 arm — assets auto-download from MuJoCo Menagerie
+sim = Robot("so100")
+
+# Inspect the world
+state = sim.get_state()
+print(state["content"][0]["text"])
+
+# Step physics and render a frame
+sim.step(n_steps=100)
+frame = sim.render(width=640, height=480)
+print(f"Rendered frame: {frame['content'][0]['text']}")
+
+sim.destroy()
+print("✅ Simulation complete")
diff --git a/examples/02_sim_agent.py b/examples/02_sim_agent.py
new file mode 100644
index 0000000..95125af
--- /dev/null
+++ b/examples/02_sim_agent.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+"""The 5-Line Promise: natural language robot control.
+
+Robot() returns an AgentTool with 35+ simulation actions. Hand it to a
+Strands Agent and control the robot through conversation.
+
+Requirements:
+ pip install strands-agents strands-robots[sim]
+
+Usage:
+ python examples/02_sim_agent.py
+"""
+
+from strands import Agent
+
+from strands_robots import Robot
+
+# Factory creates a MuJoCo sim (auto-downloads assets on first run)
+robot = Robot("so100")
+
+# The sim IS the tool — pass it directly to Agent
+agent = Agent(tools=[robot])
+
+# Natural language → simulation actions
+result = agent("Get the simulation state, then run a mock policy for 1 second in fast mode")
+print(result)
+
+robot.destroy()
diff --git a/examples/03_sim_recording.py b/examples/03_sim_recording.py
new file mode 100644
index 0000000..05e1aaf
--- /dev/null
+++ b/examples/03_sim_recording.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""Record simulation rollouts as LeRobot v3 datasets.
+
+Runs a mock policy in MuJoCo, captures joint states + video, and saves
+everything as a LeRobot-compatible dataset (parquet + AV1 video).
+
+Requirements:
+ pip install strands-robots[sim]
+
+Usage:
+ python examples/03_sim_recording.py
+"""
+
+from strands_robots import Robot
+
+sim = Robot("so100")
+
+# Start recording — creates LeRobot v3 dataset structure
+sim.start_recording(
+ repo_id="local/so100_demo",
+ task="reach target",
+ fps=50,
+ root="/tmp/so100_dataset",
+)
+
+# Run a mock policy (random actions) for 2 seconds
+result = sim.run_policy(
+ robot_name="so100",
+ policy_provider="mock",
+ instruction="reach target",
+ duration=2.0,
+ fast_mode=True,
+ record_video="/tmp/so100_rollout.mp4",
+ video_fps=30,
+)
+print(result["content"][0]["text"])
+
+# Finalize the episode
+stop = sim.stop_recording()
+print(stop["content"][0]["text"])
+
+sim.destroy()
+print("✅ Dataset saved to /tmp/so100_dataset/")
diff --git a/examples/04_real_hardware.py b/examples/04_real_hardware.py
new file mode 100644
index 0000000..a17a38a
--- /dev/null
+++ b/examples/04_real_hardware.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+"""Control real robot hardware with the same factory API.
+
+Robot(mode="real") returns a HardwareRobot backed by LeRobot. The same
+Agent workflow works — just swap the mode.
+
+Requirements:
+ pip install strands-agents strands-robots[lerobot]
+ # Hardware: SO-100/SO-101 arm connected via USB (Feetech servos)
+
+Usage:
+ # Auto-detect (switches to real if USB servo controller found)
+ STRANDS_ROBOT_MODE=real python examples/04_real_hardware.py
+
+ # Or set mode explicitly in code (see below)
+"""
+
+from strands import Agent
+
+from strands_robots import Robot
+
+# Explicit real mode with camera config
+robot = Robot(
+ "so100",
+ mode="real",
+ cameras={
+ "wrist": {
+ "type": "opencv",
+ "index_or_path": "/dev/video0",
+ "fps": 15,
+ "fourcc": "MJPG",
+ },
+ },
+)
+
+# Same Agent interface as simulation
+agent = Agent(tools=[robot])
+agent("Connect to the robot, read the current joint positions, and report status")
diff --git a/examples/05_real_groot_policy.py b/examples/05_real_groot_policy.py
new file mode 100644
index 0000000..be38128
--- /dev/null
+++ b/examples/05_real_groot_policy.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+"""Run NVIDIA GR00T policy on real hardware.
+
+Starts a GR00T inference server, connects to a real SO-101 arm with
+dual cameras, and runs the policy through an Agent.
+
+Requirements:
+ pip install strands-agents strands-robots[all]
+ # Hardware: SO-101 arm + 2 USB cameras
+ # Model: Download from HuggingFace (e.g., cagataydev/gr00t-wave)
+
+Usage:
+ python examples/05_real_groot_policy.py
+"""
+
+from strands import Agent
+
+from strands_robots import Robot, gr00t_inference, lerobot_camera, pose_tool
+
+# Real robot with dual cameras
+robot = Robot(
+ "so101",
+ mode="real",
+ cameras={
+ "wrist": {
+ "type": "opencv",
+ "index_or_path": "/dev/video0",
+ "fps": 15,
+ "fourcc": "MJPG",
+ },
+ "front": {
+ "type": "opencv",
+ "index_or_path": "/dev/video2",
+ "fps": 15,
+ "fourcc": "MJPG",
+ },
+ },
+)
+
+# Build agent with robot + inference tools
+agent = Agent(
+ tools=[robot, gr00t_inference, lerobot_camera, pose_tool],
+)
+
+# Start GR00T inference server
+agent.tool.gr00t_inference(
+ action="start",
+ checkpoint_path="/data/checkpoints/gr00t-wave/checkpoint-300000",
+ port=5555,
+ data_config="so100_dualcam",
+ embodiment_tag="new_embodiment",
+)
+
+# Interactive control loop
+print("GR00T policy running. Type instructions or 'quit' to exit.")
+while True:
+ query = input("\n# ")
+ if query.lower() in ("quit", "exit", "q"):
+ break
+ agent(query)
+
+agent.tool.gr00t_inference(action="stop", port=5555)
diff --git a/examples/06_list_robots.py b/examples/06_list_robots.py
new file mode 100644
index 0000000..4066514
--- /dev/null
+++ b/examples/06_list_robots.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python3
+"""List all supported robots and their capabilities.
+
+The registry contains 38+ robots with simulation assets and/or hardware
+support. Use list_robots() to discover what's available.
+
+Requirements:
+ pip install strands-robots
+
+Usage:
+ python examples/06_list_robots.py
+"""
+
+from strands_robots import list_robots
+
+print("=== All Robots ===")
+for r in list_robots(mode="all"):
+ sim = "🎮" if r.get("has_sim") else " "
+ real = "🔧" if r.get("has_real") else " "
+ print(f" {sim} {real} {r['name']:25s} {r.get('description', '')}")
+
+print(f"\n=== Sim-only ({len(list_robots(mode='sim'))} robots) ===")
+for r in list_robots(mode="sim")[:5]:
+ print(f" {r['name']}")
+
+print(f"\n=== Real hardware ({len(list_robots(mode='real'))} robots) ===")
+for r in list_robots(mode="real"):
+ print(f" {r['name']}")
diff --git a/examples/act_policy_simulation.py b/examples/act_policy_simulation.py
new file mode 100755
index 0000000..99fc32a
--- /dev/null
+++ b/examples/act_policy_simulation.py
@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""Run a HuggingFace ACT policy in MuJoCo and export a LeRobot dataset.
+
+Downloads a pretrained ACT policy, runs it in simulation, records multi-camera
+video + joint data as a LeRobot v3 dataset. The full pipeline in ~20 lines.
+
+Requirements:
+ pip install strands-robots[sim] lerobot torch
+
+Usage:
+ python examples/act_policy_simulation.py
+"""
+
+from strands_robots import Robot
+
+# 1. Create simulated Aloha bimanual robot (14 actuators, 6 cameras)
+sim = Robot("aloha")
+
+# 2. Start recording a LeRobot dataset (parquet + AV1 video)
+sim.start_recording(
+ repo_id="local/act_aloha_sim_demo",
+ task="transfer cube",
+ fps=50,
+ root="/tmp/act_aloha_dataset",
+)
+
+# 3. Run a pretrained ACT policy from HuggingFace (51M params)
+result = sim.run_policy(
+ robot_name="aloha",
+ policy_provider="lerobot_local",
+ pretrained_name_or_path="lerobot/act_aloha_sim_transfer_cube_human",
+ instruction="transfer cube",
+ duration=2.0, # seconds of sim time
+ fast_mode=True, # no wall-clock sleep between steps
+ record_video="/tmp/act_aloha_rollout.mp4",
+)
+print(result["content"][0]["text"])
+
+# 4. Save the episode to disk
+stop = sim.stop_recording()
+print(stop["content"][0]["text"])
+
+sim.destroy()
diff --git a/examples/physics_agent.py b/examples/physics_agent.py
new file mode 100644
index 0000000..55e1a25
--- /dev/null
+++ b/examples/physics_agent.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+"""Strands Agent with full MuJoCo physics introspection.
+
+Demonstrates the new Physics API — direct Python access to MuJoCo C functions:
+ mj_ray, mj_jacBody, mj_applyFT, mj_fullM, mj_inverse, mj_contactForce,
+ mj_getState/mj_setState, mj_energyPos/mj_energyVel, and more.
+
+An agent that can reason about physics: cast rays, compute Jacobians,
+apply forces, checkpoint/restore state, read sensors, and analyze contacts
+— all through natural language.
+
+Requirements:
+ pip install strands-agents strands-robots[sim]
+
+Usage:
+ python examples/physics_agent.py
+"""
+
+from strands import Agent
+
+from strands_robots import Robot
+
+# Create a simulated SO-100 robot arm
+sim = Robot("so100")
+
+# Give the agent the simulation tool — all 50+ actions available via NL
+agent = Agent(
+ tools=[sim],
+ system_prompt=(
+ "You are a robotics physicist. You have a simulated SO-100 robot arm "
+ "in MuJoCo. Use the simulation tool to explore its physics. "
+ "Be concise and use real numbers from the simulation."
+ ),
+)
+
+# ─── Example 1: Full physics analysis in natural language ────────────────────
+print("=" * 70)
+print("Example 1: Agent-driven physics analysis")
+print("=" * 70)
+
+result = agent(
+ "Analyze the robot's physics: "
+ "1) Get the total mass breakdown, "
+ "2) Compute the mass matrix and tell me its condition number, "
+ "3) Read all sensor values, "
+ "4) Get the system energy. "
+ "Summarize the physical properties."
+)
+print(result)
+
+# ─── Example 2: Raycasting for obstacle detection ───────────────────────────
+print("\n" + "=" * 70)
+print("Example 2: Agent uses raycasting for spatial reasoning")
+print("=" * 70)
+
+result = agent(
+ "Cast rays downward from 5 points above the robot (height=1m) at "
+ "x=-0.2, -0.1, 0, 0.1, 0.2 (y=0) to map what's below. "
+ "Use multi_raycast for efficiency. Report the distance map."
+)
+print(result)
+
+# ─── Example 3: State checkpointing + force experiments ─────────────────────
+print("\n" + "=" * 70)
+print("Example 3: Save state → experiment → restore")
+print("=" * 70)
+
+result = agent(
+ "I want to experiment with forces without breaking the sim: "
+ "1) Save the current state as 'pristine', "
+ "2) Step 200 times to let things settle, "
+ "3) Get the energy, "
+ "4) Apply a 50N upward force to the robot's end-effector body, "
+ "5) Step 100 more times, "
+ "6) Get the energy again and compare, "
+ "7) Restore the 'pristine' state, "
+ "8) Verify we're back by checking energy matches the original."
+)
+print(result)
+
+# ─── Example 4: Jacobian + inverse dynamics ─────────────────────────────────
+print("\n" + "=" * 70)
+print("Example 4: Dynamics analysis")
+print("=" * 70)
+
+result = agent(
+ "Compute the Jacobian for the end-effector and run inverse dynamics. "
+ "What forces are needed at each joint to hold the current pose?"
+)
+print(result)
+
+# ─── Example 5: Contact analysis ────────────────────────────────────────────
+print("\n" + "=" * 70)
+print("Example 5: Contact force analysis")
+print("=" * 70)
+
+result = agent(
+ "Step the simulation 500 times to let everything settle, "
+ "then get detailed contact forces. "
+ "Which bodies are in contact and what are the normal forces?"
+)
+print(result)
+
+# Clean up
+sim.destroy()
+print("\n✅ Done — all physics examples complete.")
diff --git a/pyproject.toml b/pyproject.toml
index ba27df6..df97a30 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,44 +5,42 @@ build-backend = "hatchling.build"
[project]
name = "strands-robots"
dynamic = ["version"]
-description = "AI-powered robot control, simulation, and training for Strands Agents — integrates with MuJoCo, Isaac Sim, Newton, and many more"
+description = "AI-powered robot control, simulation, and training for Strands Agents"
readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12"
license = {text = "Apache-2.0"}
authors = [
{name = "AWS", email = "opensource@amazon.com"},
]
keywords = [
"robots", "agents", "strands", "sdk",
- "mujoco", "simulation", "lerobot", "reinforcement-learning",
- "gr00t", "isaac-sim", "vla",
+ "mujoco", "simulation", "lerobot",
"robot-learning", "imitation-learning",
- "teleoperation", "manipulation", "strands-agents",
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
- "Operating System :: OS Independent",
"Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3.10",
- "Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Physics",
- "Topic :: Software Development :: Libraries :: Python Modules",
]
dependencies = [
"strands-agents>=1.0.0,<2.0.0",
"numpy>=1.21.0,<3.0.0",
- "opencv-python-headless>=4.5.0,<5.0.0",
- "Pillow>=8.0.0,<12.0.0",
]
[project.optional-dependencies]
+sim = [
+ "mujoco>=3.0.0,<4.0.0",
+ "opencv-python-headless>=4.5.0,<5.0.0",
+ "Pillow>=8.0.0,<12.0.0",
+ "robot_descriptions>=1.0.0,<2.0.0",
+]
groot-service = [
"pyzmq>=27.0.0,<28.0.0",
"msgpack>=1.0.0,<2.0.0",
@@ -51,10 +49,12 @@ lerobot = [
"lerobot>=0.5.0,<0.6.0",
]
all = [
+ "strands-robots[sim]",
"strands-robots[groot-service]",
"strands-robots[lerobot]",
]
dev = [
+ "strands-robots[sim]",
"pytest>=6.0,<9.0.0",
"pytest-cov>=4.0.0,<6.0.0",
"ruff>=0.4.0,<1.0.0",
@@ -64,10 +64,8 @@ dev = [
[project.urls]
Homepage = "https://github.com/strands-labs/robots"
-Documentation = "https://github.com/strands-labs/robots#readme"
Repository = "https://github.com/strands-labs/robots.git"
Issues = "https://github.com/strands-labs/robots/issues"
-Changelog = "https://github.com/strands-labs/robots/releases"
[tool.hatch.version]
source = "vcs"
@@ -75,39 +73,13 @@ source = "vcs"
[tool.hatch.build.targets.wheel]
packages = ["strands_robots"]
-[tool.hatch.envs.default]
-dependencies = [
- "pytest>=6.0,<9.0.0",
- "pytest-cov>=4.0.0,<6.0.0",
- "ruff>=0.4.0,<1.0.0",
- "mypy>=1.0.0,<2.0.0",
- "Pillow>=8.0.0,<12.0.0",
- "requests>=2.28.0,<3.0.0",
- "msgpack>=1.0.0,<2.0.0",
- "pyzmq>=27.0.0,<28.0.0",
-]
-
-[tool.hatch.envs.default.scripts]
-test = "pytest {args:tests}"
-test-integ = "pytest tests_integ/ -v --timeout=300 {args}"
-lint = ["ruff check strands_robots tests tests_integ", "ruff format --check strands_robots tests tests_integ", "mypy strands_robots tests tests_integ"]
-format = ["ruff check --fix strands_robots tests tests_integ", "ruff format strands_robots tests tests_integ"]
-
[tool.ruff]
line-length = 120
-target-version = "py310"
+target-version = "py312"
[tool.ruff.lint]
-select = [
- "E", # pycodestyle errors
- "W", # pycodestyle warnings
- "F", # pyflakes
- "I", # isort
- "UP", # pyupgrade
-]
-ignore = [
- "E501", # line too long — handled by formatter
-]
+select = ["E", "W", "F", "I", "UP"]
+ignore = ["E501"]
[tool.ruff.lint.isort]
known-first-party = ["strands_robots"]
@@ -117,48 +89,41 @@ quote-style = "double"
indent-style = "space"
[tool.mypy]
-python_version = "3.10"
+python_version = "3.12"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true
ignore_missing_imports = false
-# Third-party libs without type stubs
[[tool.mypy.overrides]]
-module = ["lerobot.*", "gr00t.*", "draccus.*", "msgpack.*", "zmq.*", "huggingface_hub.*", "serial.*", "psutil.*", "torch.*", "torchvision.*", "transformers.*", "einops.*"]
+module = ["lerobot.*", "gr00t.*", "draccus.*", "msgpack.*", "zmq.*", "huggingface_hub.*", "serial.*", "psutil.*", "torch.*", "torchvision.*", "transformers.*", "einops.*", "mujoco.*", "robot_descriptions.*"]
ignore_missing_imports = true
-# @tool decorator injects runtime signatures mypy cannot check
[[tool.mypy.overrides]]
module = ["strands_robots.tools.*"]
disallow_untyped_defs = false
warn_return_any = false
ignore_missing_imports = true
-# LeRobot local policy uses dynamic attr access via draccus PreTrainedConfig
[[tool.mypy.overrides]]
module = ["strands_robots.policies.*"]
warn_return_any = false
disallow_untyped_defs = false
-# Robot core — complex dynamic assembly, optional deps
[[tool.mypy.overrides]]
module = ["strands_robots.robot"]
disallow_untyped_defs = false
warn_return_any = false
-# __init__.py has lazy import helpers
[[tool.mypy.overrides]]
-module = ["strands_robots.__init__"]
+module = ["strands_robots.__init__", "strands_robots.factory"]
disallow_untyped_defs = false
-# Registry modules — dynamic JSON loading returns Any
[[tool.mypy.overrides]]
module = ["strands_robots.registry.*"]
warn_return_any = false
disallow_untyped_defs = false
-# Test files — relaxed type checking for mocks, fixtures, and test utilities
[[tool.mypy.overrides]]
module = ["tests.*", "tests_integ.*"]
disallow_untyped_defs = false
@@ -166,7 +131,7 @@ warn_return_any = false
[tool.pytest.ini_options]
testpaths = ["tests"]
-addopts = "-v --cov=strands_robots --cov-report=term-missing --strict-markers"
+addopts = "-v --strict-markers"
markers = [
- "gpu: requires CUDA GPU and real model weights (run with: pytest -m gpu)",
+ "gpu: requires CUDA GPU and real model weights",
]
diff --git a/strands_robots/__init__.py b/strands_robots/__init__.py
index 8ee9c41..6c4943f 100644
--- a/strands_robots/__init__.py
+++ b/strands_robots/__init__.py
@@ -11,11 +11,12 @@
- Clean separation between robot control and policy inference
- Direct policy injection for maximum flexibility
- Multi-camera support with rich configuration options
+- MuJoCo simulation backend (no GPU required)
Lazy Loading:
- Heavy imports (Robot, tools, Gr00tPolicy) are deferred until first access.
- Heavy imports are deferred so ``import strands_robots`` stays fast when lerobot/torch
- are installed but not yet needed.
+ Heavy imports (Robot, tools, Gr00tPolicy, Simulation) are deferred until
+ first access. Heavy imports are deferred so ``import strands_robots`` stays
+ fast when lerobot/torch/mujoco are installed but not yet needed.
Light-weight symbols (Policy, MockPolicy, create_policy) are available
immediately since they don't pull in torch/lerobot.
@@ -26,7 +27,7 @@
from typing import Any
# ------------------------------------------------------------------
-# Light-weight imports — no torch / lerobot dependency
+# Light-weight imports — no torch / lerobot / mujoco dependency
# ------------------------------------------------------------------
from strands_robots.policies import MockPolicy, Policy, create_policy # noqa: F401
@@ -35,8 +36,21 @@
# ------------------------------------------------------------------
# Maps public name -> (module_path, attribute_name)
_LAZY_IMPORTS: dict[str, tuple[str, str]] = {
- "Robot": ("strands_robots.robot", "Robot"),
+ # Hardware robot
+ "Robot": ("strands_robots.factory", "Robot"),
+ "list_robots": ("strands_robots.factory", "list_robots"),
+ # Policies
"Gr00tPolicy": ("strands_robots.policies.groot", "Gr00tPolicy"),
+ # Simulation (MuJoCo)
+ "Simulation": ("strands_robots.simulation", "Simulation"),
+ "create_simulation": ("strands_robots.simulation.factory", "create_simulation"),
+ "list_backends": ("strands_robots.simulation.factory", "list_backends"),
+ "register_backend": ("strands_robots.simulation.factory", "register_backend"),
+ "SimWorld": ("strands_robots.simulation", "SimWorld"),
+ "SimRobot": ("strands_robots.simulation", "SimRobot"),
+ "SimObject": ("strands_robots.simulation", "SimObject"),
+ "SimCamera": ("strands_robots.simulation", "SimCamera"),
+ # Tools
"gr00t_inference": ("strands_robots.tools.gr00t_inference", "gr00t_inference"),
"lerobot_calibrate": ("strands_robots.tools.lerobot_calibrate", "lerobot_calibrate"),
"lerobot_camera": ("strands_robots.tools.lerobot_camera", "lerobot_camera"),
@@ -53,6 +67,11 @@
# Lazy-loaded
"Robot",
"Gr00tPolicy",
+ "Simulation",
+ "SimWorld",
+ "SimRobot",
+ "SimObject",
+ "SimCamera",
"gr00t_inference",
"lerobot_camera",
"lerobot_teleoperate",
@@ -62,12 +81,24 @@
]
+# Auto-configure MuJoCo GL backend for headless environments BEFORE any
+# module imports mujoco at the top level. MuJoCo locks the OpenGL backend
+# at import time, so MUJOCO_GL must be set first.
+try:
+ from strands_robots.simulation.mujoco.backend import _configure_gl_backend
+
+ _configure_gl_backend()
+except (ImportError, AttributeError, OSError):
+ pass
+
+
def __getattr__(name: str) -> Any: # noqa: N807
"""Lazy-load heavy modules on first attribute access.
- This avoids importing torch, lerobot, numpy, pyserial, etc. at
+ This avoids importing torch, lerobot, numpy, mujoco, pyserial, etc. at
``import strands_robots`` time. The first access to e.g.
- ``strands_robots.Robot`` triggers the real import.
+ ``strands_robots.Robot`` or ``strands_robots.Simulation`` triggers the
+ real import.
"""
if name in _LAZY_IMPORTS:
module_path, attr_name = _LAZY_IMPORTS[name]
diff --git a/strands_robots/_async_utils.py b/strands_robots/_async_utils.py
new file mode 100644
index 0000000..91819a3
--- /dev/null
+++ b/strands_robots/_async_utils.py
@@ -0,0 +1,28 @@
+"""Async-to-sync helper for resolving coroutines in sync contexts."""
+
+import asyncio
+import concurrent.futures
+
+
+def _resolve_coroutine(coro_or_result):
+ """Safely resolve a potentially-async result to a sync value.
+
+ Handles three cases:
+ 1. Already a plain value → return as-is
+ 2. Coroutine, no running loop → asyncio.run()
+ 3. Coroutine, inside running loop → offload to thread
+
+ Args:
+ coro_or_result: Either a coroutine or an already-resolved value.
+
+ Returns:
+ The resolved (sync) value.
+ """
+ if not asyncio.iscoroutine(coro_or_result):
+ return coro_or_result
+ try:
+ asyncio.get_running_loop()
+ with concurrent.futures.ThreadPoolExecutor(max_workers=1) as ex:
+ return ex.submit(asyncio.run, coro_or_result).result()
+ except RuntimeError:
+ return asyncio.run(coro_or_result)
diff --git a/strands_robots/assets/__init__.py b/strands_robots/assets/__init__.py
new file mode 100644
index 0000000..4d0d2f2
--- /dev/null
+++ b/strands_robots/assets/__init__.py
@@ -0,0 +1,284 @@
+"""Robot Asset Manager for Strands Robots Simulation.
+
+Resolves robot model files (MJCF XML) from:
+ 1. Bundled assets (``strands_robots/assets/`` — from MuJoCo Menagerie)
+ 2. Custom paths (``STRANDS_URDF_DIR`` / ``STRANDS_ASSETS_DIR`` env vars)
+ 3. User home (``~/.strands_robots/assets/``)
+
+Source: https://github.com/google-deepmind/mujoco_menagerie (Apache-2.0)
+"""
+
+import logging
+import os
+from pathlib import Path
+
+from strands_robots.registry import (
+ format_robot_table,
+ get_robot,
+ list_aliases,
+ list_robots,
+ list_robots_by_category,
+)
+from strands_robots.registry import (
+ resolve_name as resolve_robot_name,
+)
+
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────
+# Asset directory resolution
+# ─────────────────────────────────────────────────────────────────────
+
+_BUNDLED_DIR = Path(__file__).parent
+_USER_CACHE_DIR = Path.home() / ".strands_robots" / "assets"
+
+
+def get_assets_dir() -> Path:
+ """Get the primary assets directory (user cache).
+
+ Returns ``~/.strands_robots/assets/`` by default (writable, not in pip package).
+ Override with ``STRANDS_ASSETS_DIR`` env var.
+ """
+ custom = os.getenv("STRANDS_ASSETS_DIR")
+ if custom:
+ d = Path(custom)
+ else:
+ d = _USER_CACHE_DIR
+ d.mkdir(parents=True, exist_ok=True)
+ return d
+
+
+def get_search_paths() -> list[Path]:
+ """Get ordered list of asset search paths.
+
+ Order:
+ 1. User cache (``~/.strands_robots/assets/``)
+ 2. Custom paths from ``STRANDS_URDF_DIR`` / ``STRANDS_ASSETS_DIR``
+ 3. Bundled package dir (``strands_robots/assets/`` — XML only)
+ 4. CWD/assets
+ """
+ paths = []
+
+ # User cache first (where downloads go)
+ paths.append(get_assets_dir())
+
+ # Custom paths from env
+ custom = os.getenv("STRANDS_URDF_DIR") or os.getenv("STRANDS_ASSETS_DIR")
+ if custom:
+ for p in custom.split(":"):
+ cp = Path(p)
+ if cp not in paths:
+ paths.append(cp)
+
+ # Bundled directory (XML files only, no meshes in pip package)
+ if _BUNDLED_DIR not in paths:
+ paths.append(_BUNDLED_DIR)
+
+ # CWD
+ cwd_assets = Path.cwd() / "assets"
+ if cwd_assets not in paths:
+ paths.append(cwd_assets)
+
+ return paths
+
+
+# ─────────────────────────────────────────────────────────────────────
+# Model path resolution (delegates to registry)
+# ─────────────────────────────────────────────────────────────────────
+
+
+def _auto_download_robot(name: str, info: dict) -> bool:
+ """Auto-download a single robot's assets via robot_descriptions.
+
+ Called lazily when resolve_model_path finds XML but no meshes.
+ Returns True if download succeeded.
+ """
+ try:
+ from strands_robots.tools.download_assets import (
+ _download_from_github,
+ _download_via_robot_descriptions,
+ _robot_descriptions_available,
+ get_user_assets_dir,
+ )
+ except ImportError:
+ logger.debug("download_assets not available for auto-download")
+ return False
+
+ dest_dir = get_user_assets_dir()
+ canonical = resolve_robot_name(name)
+
+ # Try robot_descriptions first (covers most robots)
+ if _robot_descriptions_available():
+ results = _download_via_robot_descriptions({canonical: info}, dest_dir)
+ if results.get(canonical, "").startswith("downloaded"):
+ logger.info("Auto-downloaded %s via robot_descriptions", canonical)
+ return True
+
+ # Try custom GitHub source
+ source = info.get("asset", {}).get("source", {})
+ if source.get("type") == "github":
+ result = _download_from_github(canonical, info, dest_dir)
+ if result.startswith("downloaded"):
+ logger.info("Auto-downloaded %s from GitHub", canonical)
+ return True
+
+ return False
+
+
+def _has_meshes(directory: Path) -> bool:
+ """Check if a directory tree contains mesh files."""
+ _MESH_EXTS = {".stl", ".obj", ".msh", ".ply"}
+ return any(f.suffix.lower() in _MESH_EXTS for f in directory.rglob("*") if f.is_file())
+
+
+def resolve_model_path(
+ name: str,
+ prefer_scene: bool = False,
+) -> Path | None:
+ """Resolve a robot name to its MJCF model XML path.
+
+ Looks up the robot in ``registry/robots.json``, then searches
+ the asset directories for the actual file. If XML is found but
+ mesh files are missing, automatically downloads them via
+ ``robot_descriptions`` before returning.
+
+ Args:
+ name: Robot name (canonical or alias).
+ prefer_scene: If True, return scene XML (with ground/lights)
+ instead of bare model XML.
+
+ Returns:
+ Path to the MJCF XML file, or None if not found.
+
+ Examples::
+
+ resolve_model_path("so100") # → .../trs_so_arm100/so_arm100.xml
+ resolve_model_path("so100", prefer_scene=True) # → .../trs_so_arm100/scene.xml
+ resolve_model_path("franka") # → .../franka_emika_panda/panda.xml
+ """
+ info = get_robot(name)
+ if not info or "asset" not in info:
+ logger.warning("Unknown robot or no asset: %s", name)
+ return None
+
+ asset = info["asset"]
+ xml_file = asset["scene_xml"] if prefer_scene else asset["model_xml"]
+
+ candidates = []
+ for search_dir in get_search_paths():
+ model_path = search_dir / asset["dir"] / xml_file
+ if model_path.exists():
+ candidates.append(model_path)
+
+ if not candidates:
+ # No XML found at all — try auto-download, then re-search
+ logger.info("No XML found for %s, attempting auto-download...", name)
+ if _auto_download_robot(name, info):
+ for search_dir in get_search_paths():
+ model_path = search_dir / asset["dir"] / xml_file
+ if model_path.exists():
+ candidates.append(model_path)
+
+ if not candidates:
+ logger.warning("Robot model not found: %s → %s/%s", name, asset["dir"], xml_file)
+ return None
+
+ # Prefer the candidate whose directory contains mesh files,
+ # because an XML without meshes will fail to load in MuJoCo.
+ for path in candidates:
+ if _has_meshes(path.parent):
+ logger.debug("Resolved %s → %s (has meshes)", name, path)
+ return path
+
+ # XML found but no meshes — auto-download and re-check
+ logger.info("XML found for %s but no meshes, attempting auto-download...", name)
+ if _auto_download_robot(name, info):
+ # Re-scan after download (new symlinks may have appeared)
+ for search_dir in get_search_paths():
+ model_path = search_dir / asset["dir"] / xml_file
+ if model_path.exists() and _has_meshes(model_path.parent):
+ logger.debug("Resolved %s → %s (auto-downloaded)", name, model_path)
+ return model_path
+
+ # Final fallback: return first candidate (some robots have no meshes)
+ logger.debug("Resolved %s → %s (no meshes available)", name, candidates[0])
+ return candidates[0]
+
+
+def resolve_model_dir(name: str) -> Path | None:
+ """Resolve a robot name to its asset directory (containing XML + meshes).
+
+ Args:
+ name: Robot name (canonical or alias).
+
+ Returns:
+ Path to the robot's asset directory, or None if not found.
+ """
+ info = get_robot(name)
+ if not info or "asset" not in info:
+ return None
+
+ asset_dir = info["asset"]["dir"]
+ for search_dir in get_search_paths():
+ dir_path = search_dir / asset_dir
+ if dir_path.exists():
+ return dir_path
+ return None
+
+
+def get_robot_info(name: str) -> dict | None:
+ """Get information about a robot model.
+
+ Args:
+ name: Robot name (canonical or alias).
+
+ Returns:
+ Dict with description, category, joints, asset info, etc.
+ """
+ info = get_robot(name)
+ if info is None:
+ return None
+ result = dict(info)
+ result["canonical_name"] = resolve_robot_name(name)
+ path = resolve_model_path(name)
+ result["resolved_path"] = str(path) if path else None
+ result["available"] = path is not None
+ return result
+
+
+def list_available_robots() -> list[dict]:
+ """List all available robot models with their info.
+
+ Returns:
+ List of dicts with name, description, joints, category, available, path.
+ """
+ robots = []
+ for r in list_robots(mode="sim"):
+ path = resolve_model_path(r["name"])
+ info = get_robot(r["name"]) or {}
+ robots.append(
+ {
+ "name": r["name"],
+ "description": r.get("description", ""),
+ "joints": r.get("joints"),
+ "category": r.get("category", ""),
+ "dir": info.get("asset", {}).get("dir", ""),
+ "available": path is not None,
+ "path": str(path) if path else None,
+ }
+ )
+ return robots
+
+
+__all__ = [
+ "resolve_model_path",
+ "resolve_model_dir",
+ "resolve_robot_name",
+ "get_robot_info",
+ "list_available_robots",
+ "list_robots_by_category",
+ "list_aliases",
+ "format_robot_table",
+ "get_assets_dir",
+ "get_search_paths",
+]
diff --git a/strands_robots/assets/download.py b/strands_robots/assets/download.py
new file mode 100644
index 0000000..252fbb3
--- /dev/null
+++ b/strands_robots/assets/download.py
@@ -0,0 +1,18 @@
+"""Download robot model assets — redirects to strands_robots.tools.download_assets.
+
+The download tool is strands_robots/tools/download_assets.py and downloads to
+~/.strands_robots/assets/ (user cache) instead of the bundled package dir.
+"""
+
+from strands_robots.tools.download_assets import (
+ _needs_download, # noqa: F401
+ download_assets,
+ download_robots,
+ get_user_assets_dir,
+ main,
+)
+
+__all__ = ["download_assets", "download_robots", "get_user_assets_dir", "main"]
+
+if __name__ == "__main__":
+ main()
diff --git a/strands_robots/dataset_recorder.py b/strands_robots/dataset_recorder.py
new file mode 100644
index 0000000..8f25624
--- /dev/null
+++ b/strands_robots/dataset_recorder.py
@@ -0,0 +1,515 @@
+"""LeRobotDataset recorder bridge for strands-robots.
+
+Wraps LeRobotDataset so that both robot.py (real hardware) and
+simulation.py (MuJoCo) can produce training-ready datasets with
+a single add_frame() call per control step.
+
+Usage:
+ recorder = DatasetRecorder.create(
+ repo_id="user/my_dataset",
+ fps=30,
+ robot_features=robot.observation_features,
+ action_features=robot.action_features,
+ task="pick up the red cube",
+ )
+ # In control loop:
+ recorder.add_frame(observation, action, task="pick up the red cube")
+ # End of episode:
+ recorder.save_episode()
+ # Optionally:
+ recorder.push_to_hub()
+"""
+
+import functools
+import logging
+import sys
+from typing import Any
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+# ── Lazy check for LeRobot availability ──────────────────────────────
+# We must NOT import lerobot at module level because it pulls in
+# `datasets` → `pandas`, which can crash with a numpy ABI mismatch on
+# systems where the system pandas was compiled against an older numpy
+# (e.g. JetPack / Jetson with system pandas 2.1.4 + pip numpy 2.x).
+
+
+@functools.lru_cache(maxsize=1)
+def has_lerobot_dataset() -> bool:
+ """Check if lerobot is available. Result is cached after first call."""
+ try:
+ from lerobot.datasets.lerobot_dataset import LeRobotDataset # noqa: F401
+
+ return True
+ except (ImportError, ValueError, RuntimeError) as exc:
+ logger.debug("lerobot not available: %s", exc)
+ return False
+
+
+def _get_lerobot_dataset_class():
+ """Import and return LeRobotDataset class, or raise ImportError.
+
+ Supports test mocking: if ``strands_robots.dataset_recorder.LeRobotDataset``
+ has been set (by a test mock), returns that class directly.
+ """
+ # Support test mocking: check module-level overrides
+ this_module = sys.modules[__name__]
+
+ # If a test injected a mock LeRobotDataset class, use it
+ mock_cls = getattr(this_module, "LeRobotDataset", None)
+ if mock_cls is not None:
+ return mock_cls
+
+ # Actual import
+ try:
+ from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+ return LeRobotDataset
+ except (ImportError, ValueError, RuntimeError) as exc:
+ raise ImportError(
+ f"lerobot not available ({exc}). Install with: pip install lerobot\nRequired for LeRobotDataset recording."
+ ) from exc
+
+
+def _numpy_ify(v):
+ """Convert any value to numpy-friendly format for add_frame."""
+ if hasattr(v, "numpy"):
+ return v.numpy()
+ if hasattr(v, "tolist") and isinstance(v, np.ndarray):
+ return v
+ if isinstance(v, (int, float)):
+ return np.array([v], dtype=np.float32)
+ if isinstance(v, list):
+ return np.array(v, dtype=np.float32)
+ return v
+
+
+class DatasetRecorder:
+ """Bridge between strands-robots control loops and LeRobotDataset.
+
+ Handles the full lifecycle:
+ 1. create() — build LeRobotDataset with correct features
+ 2. add_frame() — called every control step with obs + action
+ 3. save_episode() — finalize episode (encodes video, writes parquet)
+ 4. push_to_hub() — upload to HuggingFace
+
+ Works for both real hardware (robot.py) and simulation (simulation.py).
+ """
+
+ def __init__(self, dataset, task: str = ""):
+ self.dataset = dataset
+ self.default_task = task
+ self.frame_count = 0
+ self.dropped_frame_count = 0
+ self.episode_count = 0
+ self._closed = False
+ self._cached_state_keys: list[str] | None = None
+ self._cached_action_keys: list[str] | None = None
+
+ @classmethod
+ def create(
+ cls,
+ repo_id: str,
+ fps: int = 30,
+ robot_type: str = "unknown",
+ robot_features: dict[str, Any] | None = None,
+ action_features: dict[str, Any] | None = None,
+ camera_keys: list[str] | None = None,
+ joint_names: list[str] | None = None,
+ task: str = "",
+ root: str | None = None,
+ use_videos: bool = True,
+ vcodec: str = "libsvtav1",
+ streaming_encoding: bool = True,
+ image_writer_threads: int = 4,
+ video_backend: str = "auto",
+ ) -> "DatasetRecorder":
+ """Create a new DatasetRecorder with auto-detected features.
+
+ Args:
+ repo_id: HuggingFace dataset ID (e.g. "user/my_dataset")
+ fps: Recording frame rate
+ robot_type: Robot type string (e.g. "so100", "panda")
+ robot_features: Dict of observation feature names → types
+ (from robot.observation_features or sim joint names)
+ action_features: Dict of action feature names → types
+ camera_keys: List of camera names (images become video features)
+ joint_names: List of joint names (alternative to robot_features for sim)
+ task: Default task description
+ root: Local directory for dataset storage
+ use_videos: Encode camera frames as video (True) or keep as images
+ vcodec: Video codec (h264, hevc, libsvtav1)
+ streaming_encoding: Stream-encode video during capture
+ image_writer_threads: Threads for writing image frames
+ video_backend: Video backend for encoding ("auto" for HW encoder auto-detect)
+ """
+ # Lazy import — this is where we actually need lerobot
+ LeRobotDatasetCls = _get_lerobot_dataset_class()
+
+ # Build features dict in LeRobot format
+ features = cls._build_features(
+ robot_features=robot_features,
+ action_features=action_features,
+ camera_keys=camera_keys,
+ joint_names=joint_names,
+ use_videos=use_videos,
+ )
+
+ logger.info(f"Creating LeRobotDataset: {repo_id} @ {fps}fps, {len(features)} features, robot_type={robot_type}")
+
+ # Build kwargs, skip unsupported params for this LeRobot version
+ create_kwargs = dict(
+ repo_id=repo_id,
+ fps=fps,
+ root=root,
+ robot_type=robot_type,
+ features=features,
+ use_videos=use_videos,
+ image_writer_threads=image_writer_threads,
+ vcodec=vcodec,
+ )
+ # streaming_encoding only in newer LeRobot versions
+ import inspect
+
+ create_sig = inspect.signature(LeRobotDatasetCls.create)
+ if "streaming_encoding" in create_sig.parameters:
+ create_kwargs["streaming_encoding"] = streaming_encoding
+ if "video_backend" in create_sig.parameters:
+ create_kwargs["video_backend"] = video_backend
+ dataset = LeRobotDatasetCls.create(**create_kwargs)
+
+ recorder = cls(dataset=dataset, task=task)
+ logger.info("DatasetRecorder ready: %s", repo_id)
+ return recorder
+
+ @classmethod
+ def _build_features(
+ cls,
+ robot_features: dict | None = None,
+ action_features: dict | None = None,
+ camera_keys: list[str] | None = None,
+ joint_names: list[str] | None = None,
+ use_videos: bool = True,
+ ) -> dict[str, Any]:
+ """Build LeRobot v3-compatible features dict.
+
+ LeRobot v3 features format:
+ {
+ "observation.images.camera_name": {"dtype": "video", "shape": (C, H, W), "names": [...]},
+ "observation.state": {"dtype": "float32", "shape": (N,), "names": [...]},
+ "action": {"dtype": "float32", "shape": (N,), "names": [...]},
+ }
+
+ Note: "names" must be a flat list of strings, NOT a dict like {"motors": [...]}.
+ """
+ features = {}
+
+ # --- Observation: cameras → video/image features ---
+ if camera_keys:
+ for cam_name in camera_keys:
+ key = f"observation.images.{cam_name}"
+ dtype = "video" if use_videos else "image"
+ features[key] = {
+ "dtype": dtype,
+ "shape": (
+ 3,
+ 480,
+ 640,
+ ), # CHW default, actual shape set on first frame
+ "names": ["channels", "height", "width"],
+ }
+
+ # --- Observation: state (joint positions) ---
+ state_dim = 0
+ state_names = []
+ if robot_features:
+ # Count scalar features (exclude cameras)
+ state_keys = [
+ k
+ for k, v in robot_features.items()
+ if not isinstance(v, dict) or v.get("dtype") not in ("image", "video")
+ ]
+ state_dim = len(state_keys)
+ state_names = state_keys
+ elif joint_names:
+ state_dim = len(joint_names)
+ state_names = list(joint_names)
+
+ if state_dim > 0:
+ features["observation.state"] = {
+ "dtype": "float32",
+ "shape": (state_dim,),
+ "names": state_names,
+ }
+
+ # --- Action ---
+ action_dim = 0
+ action_names = []
+ if action_features:
+ action_keys = [
+ k
+ for k, v in action_features.items()
+ if not isinstance(v, dict) or v.get("dtype") not in ("image", "video")
+ ]
+ action_dim = len(action_keys)
+ action_names = action_keys
+ elif joint_names:
+ action_dim = len(joint_names)
+ action_names = list(joint_names)
+ elif state_dim > 0:
+ action_dim = state_dim # Same dim as state by default
+ action_names = state_names[:]
+
+ if action_dim > 0:
+ features["action"] = {
+ "dtype": "float32",
+ "shape": (action_dim,),
+ "names": action_names[:action_dim],
+ }
+
+ return features
+
+ def add_frame(
+ self,
+ observation: dict[str, Any],
+ action: dict[str, Any],
+ task: str | None = None,
+ camera_keys: list[str] | None = None,
+ ) -> None:
+ """Add a single control-loop frame to the dataset.
+
+ This is the key method — called every step in the control loop.
+
+ Args:
+ observation: Raw observation dict from robot/sim
+ (joint_name → float, camera_name → np.ndarray)
+ action: Action dict (joint_name → float)
+ task: Task description (uses default if None)
+ camera_keys: Which keys in observation are camera images
+ """
+ if self._closed:
+ return
+
+ frame = {}
+
+ # --- Detect camera vs state keys ---
+ if camera_keys is None:
+ camera_keys = [k for k, v in observation.items() if isinstance(v, np.ndarray) and v.ndim >= 2]
+
+ state_keys = [k for k in observation.keys() if k not in camera_keys]
+
+ # --- Camera images → observation.images.{name} ---
+ for cam_key in camera_keys:
+ img = observation[cam_key]
+ if isinstance(img, np.ndarray):
+ # LeRobot expects HWC uint8 for add_frame
+ if img.dtype != np.uint8:
+ img = (np.clip(img, 0, 1) * 255).astype(np.uint8)
+ frame[f"observation.images.{cam_key}"] = img
+
+ # --- State → observation.state (flattened vector) ---
+ # Use feature schema ordering to match the dataset schema declared in _build_features().
+ if state_keys:
+ state_vals = []
+ if self._cached_state_keys is None:
+ feat = self.dataset.features.get("observation.state", {})
+ state_names = feat.get("names", []) if isinstance(feat, dict) else getattr(feat, "names", [])
+ self._cached_state_keys = state_names if state_names else sorted(state_keys)
+
+ for k in self._cached_state_keys:
+ v = observation.get(k)
+ if v is None:
+ state_vals.append(0.0)
+ elif isinstance(v, (int, float)):
+ state_vals.append(float(v))
+ elif isinstance(v, np.ndarray) and v.ndim == 0:
+ state_vals.append(float(v))
+ elif isinstance(v, (list, np.ndarray)):
+ arr = np.asarray(v, dtype=np.float32).flatten()
+ state_vals.extend(arr.tolist())
+ if state_vals:
+ frame["observation.state"] = np.array(state_vals, dtype=np.float32)
+
+ # --- Action → flattened vector ---
+ # Use feature schema ordering for actions too.
+ if action:
+ action_vals = []
+ if self._cached_action_keys is None:
+ feat = self.dataset.features.get("action", {})
+ action_names = feat.get("names", []) if isinstance(feat, dict) else getattr(feat, "names", [])
+ self._cached_action_keys = action_names if action_names else sorted(action.keys())
+
+ for k in self._cached_action_keys:
+ v = action.get(k)
+ if v is None:
+ action_vals.append(0.0)
+ elif isinstance(v, (int, float)):
+ action_vals.append(float(v))
+ elif isinstance(v, np.ndarray) and v.ndim == 0:
+ action_vals.append(float(v))
+ elif isinstance(v, (list, np.ndarray)):
+ arr = np.asarray(v, dtype=np.float32).flatten()
+ action_vals.extend(arr.tolist())
+ if action_vals:
+ frame["action"] = np.array(action_vals, dtype=np.float32)
+
+ # --- Task (mandatory for LeRobot v3) ---
+ frame["task"] = task or self.default_task or "untitled"
+
+ # --- Reconcile camera keys between frame and feature schema ---
+ # Only strip *undeclared* cameras from the frame (keys present in obs
+ # but not registered in _build_features). This avoids LeRobot's
+ # "Extra features" error. Declared-but-missing cameras (e.g. when a
+ # render fails) are left alone — LeRobot tolerates absent columns and
+ # the episode simply won't have that camera's data.
+ declared_cam_keys = {k for k in self.dataset.features if k.startswith("observation.images.")}
+ frame_cam_keys = {k for k in frame if k.startswith("observation.images.")}
+ for extra in frame_cam_keys - declared_cam_keys:
+ del frame[extra]
+
+ # --- Add to dataset ---
+ try:
+ self.dataset.add_frame(frame)
+ self.frame_count += 1
+ except Exception as e:
+ self.dropped_frame_count += 1
+ n = self.dropped_frame_count
+ # Log at 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, then every 1000
+ if (n & (n - 1)) == 0 or n % 1000 == 0:
+ logger.warning(
+ "add_frame failed (frame %d, dropped %d): %s",
+ self.frame_count,
+ self.dropped_frame_count,
+ e,
+ )
+
+ def save_episode(self) -> dict[str, Any]:
+ """Finalize current episode — writes parquet, encodes video, computes stats.
+
+ LeRobot v3: save_episode() takes no task argument. Tasks are stored
+ per-frame in the episode buffer via add_frame().
+
+ Returns:
+ Dict with episode info
+ """
+ if self._closed:
+ return {"status": "error", "message": "Recorder closed"}
+
+ try:
+ self.dataset.save_episode()
+ self.episode_count += 1
+ ep_frames = self.frame_count # Total frames so far
+ logger.info(f"Episode {self.episode_count} saved: {ep_frames} total frames")
+ return {
+ "status": "success",
+ "episode": self.episode_count,
+ "total_frames": ep_frames,
+ }
+ except Exception as e:
+ logger.error("save_episode failed: %s", e)
+ return {"status": "error", "message": str(e)}
+
+ def finalize(self) -> None:
+ """Finalize the dataset (close parquet writers, flush metadata)."""
+ if self._closed:
+ return
+ try:
+ self.dataset.finalize()
+ except Exception as e:
+ logger.warning("finalize warning: %s", e)
+ self._closed = True
+
+ def push_to_hub(
+ self,
+ tags: list[str] | None = None,
+ private: bool = False,
+ ) -> dict[str, Any]:
+ """Push dataset to HuggingFace Hub.
+
+ Args:
+ tags: Optional tags for the dataset
+ private: Upload as private dataset
+
+ Returns:
+ Dict with push status
+ """
+ try:
+ self.dataset.push_to_hub(tags=tags, private=private)
+ logger.info("Dataset pushed to hub: %s", self.dataset.repo_id)
+ return {
+ "status": "success",
+ "repo_id": self.dataset.repo_id,
+ "episodes": self.episode_count,
+ "frames": self.frame_count,
+ }
+ except Exception as e:
+ logger.error("push_to_hub failed: %s", e)
+ return {"status": "error", "message": str(e)}
+
+ @property
+ def repo_id(self) -> str:
+ return self.dataset.repo_id
+
+ @property
+ def root(self) -> str:
+ return str(self.dataset.root)
+
+ def __repr__(self) -> str:
+ return f"DatasetRecorder(repo_id={self.repo_id}, episodes={self.episode_count}, frames={self.frame_count})"
+
+
+# ── Shared replay-episode helpers ────────────────────────────────────
+
+
+def load_lerobot_episode(repo_id: str, episode: int = 0, root: str | None = None):
+ """Load a LeRobotDataset and resolve the frame range for an episode.
+
+ Returns:
+ Tuple of (dataset, episode_start, episode_length) on success.
+
+ Raises:
+ ImportError: If lerobot is not installed.
+ ValueError: If the episode is out of range or has no frames.
+ """
+ from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+ ds = LeRobotDataset(repo_id=repo_id, root=root)
+
+ num_episodes = ds.meta.total_episodes if hasattr(ds.meta, "total_episodes") else len(ds.meta.episodes)
+ if episode >= num_episodes:
+ raise ValueError(f"Episode {episode} out of range (0-{num_episodes - 1})")
+
+ episode_start = 0
+ episode_length = 0
+ try:
+ if hasattr(ds, "episode_data_index"):
+ from_idx = ds.episode_data_index["from"][episode].item()
+ to_idx = ds.episode_data_index["to"][episode].item()
+ episode_start = from_idx
+ episode_length = to_idx - from_idx
+ else:
+ for i in range(episode):
+ ep_info = ds.meta.episodes[i] if hasattr(ds.meta, "episodes") else {}
+ episode_start += ep_info.get("length", 0)
+ ep_info = ds.meta.episodes[episode] if hasattr(ds.meta, "episodes") else {}
+ episode_length = ep_info.get("length", 0)
+ except Exception:
+ # Last resort: scan frames to find episode boundaries
+ for idx in range(len(ds)):
+ frame = ds[idx]
+ frame_ep = frame.get("episode_index", -1) if hasattr(frame, "get") else -1
+ if hasattr(frame_ep, "item"):
+ frame_ep = frame_ep.item()
+ if frame_ep == episode:
+ if episode_length == 0:
+ episode_start = idx
+ episode_length += 1
+ elif episode_length > 0:
+ break
+
+ if episode_length == 0:
+ raise ValueError(f"Episode {episode} has no frames")
+
+ return ds, episode_start, episode_length
diff --git a/strands_robots/factory.py b/strands_robots/factory.py
new file mode 100644
index 0000000..778bd03
--- /dev/null
+++ b/strands_robots/factory.py
@@ -0,0 +1,199 @@
+"""Unified Robot Factory — convenience layer over Simulation and HardwareRobot.
+
+Provides:
+ - ``Robot("so100")`` → auto-detects sim/real, returns the right backend
+ - ``list_robots()`` → what's available
+
+Examples::
+
+ # Auto-detect (sim if no hardware found)
+ sim = Robot("so100")
+
+ # Explicit sim
+ sim = Robot("so100", mode="sim")
+
+ # With custom URDF/MJCF path
+ sim = Robot("my_arm", mode="sim", urdf_path="/path/to/robot.xml")
+
+ # Real hardware
+ hw = Robot("so100", mode="real", cameras={...})
+
+Future (not yet implemented)::
+
+ sim = Robot("unitree_go2", backend="isaac", num_envs=4096)
+ sim = Robot("so100", backend="newton", num_envs=4096)
+"""
+
+import logging
+import os
+from typing import Any
+
+from strands_robots.registry import (
+ get_hardware_type,
+ has_hardware,
+ resolve_name,
+)
+from strands_robots.registry import list_robots as _registry_list_robots
+
+logger = logging.getLogger(__name__)
+
+
+def _auto_detect_mode(canonical: str) -> str:
+ """Auto-detect sim vs real mode.
+
+ Priority:
+ 1. ``STRANDS_ROBOT_MODE`` env var (explicit override)
+ 2. Robot-specific USB detection (Feetech/Dynamixel servo controllers)
+ 3. Default to sim (safest — never accidentally send commands to hardware)
+ """
+ env_mode = os.getenv("STRANDS_ROBOT_MODE", "").lower()
+ if env_mode in ("sim", "real"):
+ return env_mode
+
+ # Only probe USB if the robot actually has hardware support
+ if has_hardware(canonical):
+ try:
+ import serial.tools.list_ports
+
+ ports = list(serial.tools.list_ports.comports())
+ servo_keywords = ["feetech", "dynamixel", "sts3215", "xl430", "xl330"]
+ exclude = ["bluetooth", "internal", "debug", "apple", "modem"]
+ robot_ports = [
+ p
+ for p in ports
+ if any(kw in (p.description + getattr(p, "manufacturer", "")).lower() for kw in servo_keywords)
+ and not any(s in p.description.lower() for s in exclude)
+ ]
+ if robot_ports:
+ logger.info(
+ "Auto-detected robot hardware: %s",
+ [p.device for p in robot_ports],
+ )
+ return "real"
+ except (ImportError, Exception):
+ pass
+
+ return "sim"
+
+
+def Robot(
+ name: str,
+ mode: str = "auto",
+ backend: str = "mujoco",
+ urdf_path: str = None,
+ cameras: dict[str, dict[str, Any]] | None = None,
+ position: list[float] | None = None,
+ **kwargs,
+):
+ """Create a robot — returns a Simulation or HardwareRobot instance.
+
+ This is a convenience factory, NOT a wrapper class. You get the real
+ backend instance back — with full access to all its methods.
+
+ Args:
+ name: Robot name ("so100", "aloha", "unitree_g1", "panda", ...)
+ Accepts any alias defined in ``registry/robots.json``.
+ mode: "auto" (detect hardware), "sim", or "real".
+ backend: Simulation backend — currently only "mujoco" (CPU).
+ Future: "isaac" (GPU), "newton" (GPU).
+ urdf_path: Explicit path to URDF/MJCF file. If not provided,
+ resolved via the model registry (asset manager or
+ STRANDS_URDF_DIR search paths).
+ cameras: Camera config for real hardware. Example::
+
+ {"wrist": {"type": "opencv", "index_or_path": "/dev/video0", "fps": 30}}
+
+ position: Robot position in sim world [x, y, z].
+ **kwargs: Forwarded to the underlying backend constructor.
+
+ Returns:
+ ``Simulation`` (MuJoCo sim) or ``Robot`` (real hardware).
+
+ Raises:
+ RuntimeError: If the sim world or robot fails to initialize.
+ NotImplementedError: If an unimplemented backend is requested.
+
+ Examples::
+
+ # MuJoCo sim (auto — no hardware detected)
+ sim = Robot("so100")
+ sim = Robot("so100", mode="sim")
+
+ # Explicit MJCF model path
+ sim = Robot("my_arm", mode="sim", urdf_path="path/to/robot.xml")
+
+ # Real hardware
+ hw = Robot("so100", mode="real", cameras={...})
+
+ # The 5-line promise
+ from strands_robots import Robot
+ from strands import Agent
+ robot = Robot("so100")
+ agent = Agent(tools=[robot])
+ agent("Pick up the red cube")
+ """
+ canonical = resolve_name(name)
+
+ if mode == "auto":
+ mode = _auto_detect_mode(canonical)
+
+ # ── Simulation ──
+ if mode == "sim":
+ if backend != "mujoco":
+ raise NotImplementedError(
+ f"Backend {backend!r} is not yet implemented. "
+ f"Currently supported: 'mujoco'. "
+ f"Isaac and Newton backends are on the roadmap."
+ )
+
+ from strands_robots.simulation import Simulation
+
+ sim = Simulation(
+ tool_name=f"{canonical}_sim",
+ **kwargs,
+ )
+ sim._dispatch_action("create_world", {})
+
+ # Build add_robot params — pass urdf_path if the user provided one
+ add_robot_params: dict[str, Any] = {
+ "robot_name": canonical,
+ "data_config": canonical,
+ "position": position or [0.0, 0.0, 0.0],
+ }
+ if urdf_path:
+ add_robot_params["urdf_path"] = urdf_path
+
+ result = sim._dispatch_action("add_robot", add_robot_params)
+ if result.get("status") == "error":
+ # Extract human-readable message from content
+ content = result.get("content", [])
+ msg = content[0].get("text", str(result)) if content else str(result)
+ raise RuntimeError(f"Failed to create sim robot '{canonical}': {msg}")
+ return sim
+
+ # ── Real hardware ──
+ else:
+ from strands_robots.robot import Robot as HardwareRobot
+
+ real_type = get_hardware_type(canonical) or canonical
+ return HardwareRobot(
+ tool_name=canonical,
+ robot=real_type,
+ cameras=cameras,
+ **kwargs,
+ )
+
+
+def list_robots(mode: str = "all") -> list[dict[str, Any]]:
+ """List available robots.
+
+ Args:
+ mode: "all", "sim", "real", or "both" (has both sim and real).
+
+ Returns:
+ List of dicts with name, description, has_sim, has_real.
+ """
+ return _registry_list_robots(mode)
+
+
+__all__ = ["Robot", "list_robots"]
diff --git a/strands_robots/policies/lerobot_local/policy.py b/strands_robots/policies/lerobot_local/policy.py
index 5cbd682..4c3ff1a 100644
--- a/strands_robots/policies/lerobot_local/policy.py
+++ b/strands_robots/policies/lerobot_local/policy.py
@@ -886,17 +886,28 @@ def _build_batch_from_strands_format(
state_values.append(float(value))
if state_values:
- # Validate state dimension matches what the model expects.
- # Dimension mismatches cause shape errors in the forward pass.
+ # Auto-adapt state dimension to match what the model expects.
+ # Robots may expose more joints than the policy was trained on
+ # (e.g. aloha has 16 joints but ACT expects 14). Truncate excess
+ # or zero-pad if fewer, rather than raising an error.
state_feature = self._input_features.get("observation.state")
if state_feature:
expected_dim = state_feature.shape[0] if hasattr(state_feature, "shape") else len(state_values)
- if len(state_values) != expected_dim:
- raise ValueError(
- f"State dimension mismatch: got {len(state_values)} values from "
- f"robot_state_keys but model expects {expected_dim}. "
- f"Check that robot_state_keys matches your robot's actual joint count."
+ if len(state_values) > expected_dim:
+ logger.debug(
+ "State dim %d > model expects %d — truncating to first %d values",
+ len(state_values),
+ expected_dim,
+ expected_dim,
)
+ state_values = state_values[:expected_dim]
+ elif len(state_values) < expected_dim:
+ logger.debug(
+ "State dim %d < model expects %d — zero-padding",
+ len(state_values),
+ expected_dim,
+ )
+ state_values.extend([0.0] * (expected_dim - len(state_values)))
batch["observation.state"] = torch.tensor(state_values, dtype=torch.float32).unsqueeze(0).to(self._device)
# Map camera images to model's image input features.
diff --git a/strands_robots/simulation/__init__.py b/strands_robots/simulation/__init__.py
new file mode 100644
index 0000000..57a0746
--- /dev/null
+++ b/strands_robots/simulation/__init__.py
@@ -0,0 +1,131 @@
+"""Strands Robots Simulation — multi-backend simulation framework.
+
+Architecture::
+
+ simulation/
+ ├── __init__.py ← this file (re-exports, Simulation alias)
+ ├── base.py ← SimulationBackend ABC
+ ├── factory.py ← create_simulation() + backend registration
+ ├── models.py ← shared dataclasses (SimWorld, SimRobot, ...)
+ ├── model_registry.py ← URDF/MJCF resolution (shared across backends)
+ └── mujoco/ ← MuJoCo CPU backend
+ ├── __init__.py
+ ├── backend.py ← lazy mujoco import + GL config
+ ├── mjcf_builder.py ← MJCF XML builder
+ ├── physics.py ← advanced physics (raycasting, jacobians, forces)
+ ├── scene_ops.py ← XML round-trip inject/eject
+ ├── rendering.py ← render RGB/depth, observations
+ ├── policy_runner.py ← run_policy, eval_policy, replay
+ ├── randomization.py ← domain randomization
+ ├── recording.py ← LeRobotDataset recording
+ ├── tool_spec.json ← AgentTool input schema
+ └── simulation.py ← Simulation (AgentTool orchestrator)
+
+Usage::
+
+ # Default (MuJoCo) via factory
+ from strands_robots.simulation import create_simulation
+ sim = create_simulation()
+
+ # Direct class access
+ from strands_robots.simulation import Simulation
+ sim = Simulation()
+
+ # Explicit backend
+ from strands_robots.simulation.mujoco import MuJoCoSimulation
+
+ # Shared types (no heavy deps)
+ from strands_robots.simulation import SimWorld, SimRobot, SimObject
+
+ # ABC for custom backends
+ from strands_robots.simulation.base import SimulationBackend
+
+Future backends::
+
+ from strands_robots.simulation.isaac import IsaacSimulation
+ from strands_robots.simulation.newton import NewtonSimulation
+"""
+
+import importlib as _importlib
+from typing import Any
+
+# --- Light imports (no heavy deps — stdlib + dataclasses only) ---
+from strands_robots.simulation.base import SimulationBackend
+from strands_robots.simulation.factory import (
+ create_simulation,
+ list_backends,
+ register_backend,
+)
+from strands_robots.simulation.model_registry import (
+ list_available_models,
+ list_registered_urdfs,
+ register_urdf,
+ resolve_model,
+ resolve_urdf,
+)
+from strands_robots.simulation.models import (
+ SimCamera,
+ SimObject,
+ SimRobot,
+ SimStatus,
+ SimWorld,
+ TrajectoryStep,
+)
+
+# --- Heavy imports (lazy — need strands SDK + mujoco) ---
+_LAZY_IMPORTS: dict[str, tuple[str, str]] = {
+ "Simulation": ("strands_robots.simulation.mujoco.simulation", "Simulation"),
+ "MuJoCoSimulation": ("strands_robots.simulation.mujoco.simulation", "Simulation"),
+ "MJCFBuilder": ("strands_robots.simulation.mujoco.mjcf_builder", "MJCFBuilder"),
+ "_configure_gl_backend": ("strands_robots.simulation.mujoco.backend", "_configure_gl_backend"),
+ "_ensure_mujoco": ("strands_robots.simulation.mujoco.backend", "_ensure_mujoco"),
+ "_is_headless": ("strands_robots.simulation.mujoco.backend", "_is_headless"),
+}
+
+
+__all__ = [
+ # ABC
+ "SimulationBackend",
+ # Factory
+ "create_simulation",
+ "list_backends",
+ "register_backend",
+ # Default backend alias
+ "Simulation",
+ "MuJoCoSimulation",
+ # Shared dataclasses
+ "SimStatus",
+ "SimRobot",
+ "SimObject",
+ "SimCamera",
+ "SimWorld",
+ "TrajectoryStep",
+ # MuJoCo builder
+ "MJCFBuilder",
+ # Model registry
+ "register_urdf",
+ "resolve_model",
+ "resolve_urdf",
+ "list_registered_urdfs",
+ "list_available_models",
+]
+
+
+def __getattr__(name: str) -> Any:
+ if name in _LAZY_IMPORTS:
+ module_path, attr_name = _LAZY_IMPORTS[name]
+ module = _importlib.import_module(module_path)
+ value = getattr(module, attr_name)
+ globals()[name] = value
+ return value
+ raise AttributeError(f"module 'strands_robots.simulation' has no attribute {name!r}")
+
+
+# Auto-configure MuJoCo GL backend for headless environments.
+# Must run before any `import mujoco` — safe to call multiple times.
+try:
+ from strands_robots.simulation.mujoco.backend import _configure_gl_backend as _cfg
+
+ _cfg()
+except (ImportError, AttributeError, OSError):
+ pass
diff --git a/strands_robots/simulation/base.py b/strands_robots/simulation/base.py
new file mode 100644
index 0000000..ea1085e
--- /dev/null
+++ b/strands_robots/simulation/base.py
@@ -0,0 +1,173 @@
+"""Simulation ABC — backend-agnostic interface for all simulation backends.
+
+Every simulation backend (MuJoCo, Isaac, Newton) implements this interface.
+Agent tools and the Robot() factory interact through these methods only —
+they never touch backend-specific APIs directly.
+
+Usage::
+
+ from strands_robots.simulation import Simulation # returns MuJoCo by default
+
+ # Or explicitly:
+ from strands_robots.simulation.mujoco import MuJoCoSimulation
+
+ # Future:
+ from strands_robots.simulation.isaac import IsaacSimulation
+ from strands_robots.simulation.newton import NewtonSimulation
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any
+
+
+class SimulationBackend(ABC):
+ """Abstract base class for simulation backends.
+
+ Defines the contract that all backends (MuJoCo, Isaac, Newton) must
+ implement. This is the *programmatic* API — the AgentTool layer
+ wraps it with tool_spec/stream for LLM access.
+
+ Lifecycle::
+
+ sim = SomeBackend()
+ sim.create_world()
+ sim.add_robot("so100", data_config="so100")
+ sim.add_object("cube", shape="box", position=[0.3, 0, 0.05])
+
+ # Control loop
+ obs = sim.get_observation("so100")
+ sim.send_action({"joint_0": 0.5}, robot_name="so100")
+ sim.step(n_steps=10)
+
+ # Render
+ result = sim.render(camera_name="default")
+
+ # Cleanup
+ sim.destroy()
+ """
+
+ # --- World lifecycle ---
+
+ @abstractmethod
+ def create_world(
+ self,
+ timestep: float = None,
+ gravity: list[float] = None,
+ ground_plane: bool = True,
+ ) -> dict[str, Any]:
+ """Create a new simulation world."""
+ ...
+
+ @abstractmethod
+ def destroy(self) -> dict[str, Any]:
+ """Destroy the simulation world and release resources."""
+ ...
+
+ @abstractmethod
+ def reset(self) -> dict[str, Any]:
+ """Reset simulation to initial state."""
+ ...
+
+ @abstractmethod
+ def step(self, n_steps: int = 1) -> dict[str, Any]:
+ """Advance simulation by n physics steps."""
+ ...
+
+ @abstractmethod
+ def get_state(self) -> dict[str, Any]:
+ """Get full simulation state summary."""
+ ...
+
+ # --- Robot management ---
+
+ @abstractmethod
+ def add_robot(
+ self,
+ name: str,
+ urdf_path: str = None,
+ data_config: str = None,
+ position: list[float] = None,
+ orientation: list[float] = None,
+ ) -> dict[str, Any]:
+ """Add a robot to the simulation."""
+ ...
+
+ @abstractmethod
+ def remove_robot(self, name: str) -> dict[str, Any]:
+ """Remove a robot from the simulation."""
+ ...
+
+ # --- Object management ---
+
+ @abstractmethod
+ def add_object(
+ self,
+ name: str,
+ shape: str = "box",
+ position: list[float] = None,
+ size: list[float] = None,
+ color: list[float] = None,
+ mass: float = 0.1,
+ is_static: bool = False,
+ **kwargs,
+ ) -> dict[str, Any]:
+ """Add an object to the scene."""
+ ...
+
+ @abstractmethod
+ def remove_object(self, name: str) -> dict[str, Any]:
+ """Remove an object from the scene."""
+ ...
+
+ # --- Observation / Action ---
+
+ @abstractmethod
+ def get_observation(self, robot_name: str = None, camera_name: str = None) -> dict[str, Any]:
+ """Get observation from simulation (Robot ABC compatible)."""
+ ...
+
+ @abstractmethod
+ def send_action(self, action: dict[str, Any], robot_name: str = None, n_substeps: int = 1) -> None:
+ """Apply action to simulation (Robot ABC compatible)."""
+ ...
+
+ # --- Rendering ---
+
+ @abstractmethod
+ def render(self, camera_name: str = "default", width: int = None, height: int = None) -> dict[str, Any]:
+ """Render a camera view."""
+ ...
+
+ # --- Optional overrides (have default no-op implementations) ---
+
+ def load_scene(self, scene_path: str) -> dict[str, Any]:
+ """Load a complete scene from file. Override per backend."""
+ return {"status": "error", "content": [{"text": "load_scene not supported by this backend"}]}
+
+ def run_policy(self, robot_name: str, policy_provider: str = "mock", **kwargs) -> dict[str, Any]:
+ """Run a policy loop. Override per backend."""
+ return {"status": "error", "content": [{"text": "run_policy not supported by this backend"}]}
+
+ def randomize(self, **kwargs) -> dict[str, Any]:
+ """Apply domain randomization. Override per backend."""
+ return {"status": "error", "content": [{"text": "randomize not supported by this backend"}]}
+
+ def get_contacts(self) -> dict[str, Any]:
+ """Get contact information. Override per backend."""
+ return {"status": "success", "content": [{"text": "No contact support in this backend"}]}
+
+ def cleanup(self):
+ """Release all resources. Called on __del__ / context exit."""
+ pass
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, *exc):
+ self.cleanup()
+
+ def __del__(self):
+ try:
+ self.cleanup()
+ except (FileNotFoundError, ValueError, KeyError):
+ pass # Best-effort cleanup during object destruction
diff --git a/strands_robots/simulation/factory.py b/strands_robots/simulation/factory.py
new file mode 100644
index 0000000..f389a40
--- /dev/null
+++ b/strands_robots/simulation/factory.py
@@ -0,0 +1,191 @@
+"""Simulation factory — create_simulation() and runtime backend registration.
+
+Mirrors the policy factory pattern: JSON-driven defaults with runtime
+override capability. Backends are lazy-loaded on first use.
+
+Usage::
+
+ from strands_robots.simulation import create_simulation
+
+ # Default backend (MuJoCo)
+ sim = create_simulation()
+
+ # Explicit backend
+ sim = create_simulation("mujoco", timestep=0.001)
+
+ # Future backends
+ sim = create_simulation("isaac", gpu_id=0)
+ sim = create_simulation("newton")
+
+ # Custom backend (runtime-registered)
+ from strands_robots.simulation.factory import register_backend
+ register_backend("my_sim", lambda: MySimBackend, aliases=["custom"])
+ sim = create_simulation("custom")
+"""
+
+import logging
+from collections.abc import Callable
+from typing import Any
+
+from strands_robots.simulation.base import SimulationBackend
+
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────────────────────────────
+# Built-in backend registry (lazy loaders — no imports at module load)
+# ─────────────────────────────────────────────────────────────────────
+
+_BUILTIN_BACKENDS: dict[str, tuple[str, str]] = {
+ "mujoco": (
+ "strands_robots.simulation.mujoco.simulation",
+ "Simulation",
+ ),
+ # Future:
+ # "isaac": ("strands_robots.simulation.isaac.simulation", "IsaacSimulation"),
+ # "newton": ("strands_robots.simulation.newton.simulation", "NewtonSimulation"),
+}
+
+_BUILTIN_ALIASES: dict[str, str] = {
+ "mj": "mujoco",
+ "mjc": "mujoco",
+ "mjx": "mujoco",
+ # "isaac_sim": "isaac",
+ # "isaacsim": "isaac",
+ # "nvidia": "isaac",
+}
+
+DEFAULT_BACKEND = "mujoco"
+
+# ─────────────────────────────────────────────────────────────────────
+# Runtime registration (for user-defined backends not in built-ins)
+# ─────────────────────────────────────────────────────────────────────
+
+_runtime_registry: dict[str, Callable[[], type[SimulationBackend]]] = {}
+_runtime_aliases: dict[str, str] = {}
+
+
+def register_backend(
+ name: str,
+ loader: Callable[[], type[SimulationBackend]],
+ aliases: list[str] | None = None,
+) -> None:
+ """Register a custom simulation backend at runtime.
+
+ Use this to add backends without editing source code.
+
+ Args:
+ name: Backend identifier (e.g., ``"my_physics"``).
+ loader: Zero-arg callable that returns the backend **class**
+ (not instance). Called lazily on first ``create_simulation()``.
+ aliases: Optional short names that resolve to ``name``.
+
+ Example::
+
+ from strands_robots.simulation.factory import register_backend
+
+ register_backend(
+ "bullet",
+ lambda: BulletSimulation,
+ aliases=["pybullet", "pb"],
+ )
+ sim = create_simulation("bullet")
+ """
+ _runtime_registry[name] = loader
+ if aliases:
+ for alias in aliases:
+ _runtime_aliases[alias] = name
+ logger.debug("Registered simulation backend: %s (aliases=%s)", name, aliases)
+
+
+def list_backends() -> list[str]:
+ """List all available backend names (built-in + runtime-registered).
+
+ Returns:
+ Sorted list of unique backend identifiers and aliases.
+
+ Example::
+
+ >>> list_backends()
+ ['mj', 'mjc', 'mjx', 'mujoco']
+ """
+ names: set[str] = set()
+ names.update(_BUILTIN_BACKENDS.keys())
+ names.update(_BUILTIN_ALIASES.keys())
+ names.update(_runtime_registry.keys())
+ names.update(_runtime_aliases.keys())
+ return sorted(names)
+
+
+def _resolve_name(backend: str) -> str:
+ """Resolve aliases to canonical backend name."""
+ # Runtime aliases first (user overrides win)
+ if backend in _runtime_aliases:
+ return _runtime_aliases[backend]
+ # Built-in aliases
+ if backend in _BUILTIN_ALIASES:
+ return _BUILTIN_ALIASES[backend]
+ return backend
+
+
+def _import_backend_class(name: str) -> type[SimulationBackend]:
+ """Import and return a backend class by canonical name."""
+ # 1. Runtime registry (user-registered)
+ if name in _runtime_registry:
+ cls = _runtime_registry[name]()
+ logger.debug("Loaded runtime backend: %s → %s", name, cls.__name__)
+ return cls
+
+ # 2. Built-in registry
+ if name in _BUILTIN_BACKENDS:
+ module_path, class_name = _BUILTIN_BACKENDS[name]
+ import importlib
+
+ module = importlib.import_module(module_path)
+ cls = getattr(module, class_name)
+ logger.debug("Loaded built-in backend: %s → %s.%s", name, module_path, class_name)
+ return cls
+
+ raise ValueError(f"Unknown simulation backend: {name!r}. Available: {', '.join(list_backends())}")
+
+
+def create_simulation(
+ backend: str = DEFAULT_BACKEND,
+ **kwargs: Any,
+) -> SimulationBackend:
+ """Create a simulation backend instance.
+
+ This is the primary entry point for creating simulations.
+ Backend classes are lazy-loaded on first call.
+
+ Args:
+ backend: Backend name or alias. Defaults to ``"mujoco"``.
+ Built-in: ``"mujoco"`` (aliases: ``"mj"``, ``"mjc"``, ``"mjx"``).
+ **kwargs: Backend-specific keyword arguments passed to the
+ constructor (e.g., ``tool_name``, ``timestep``).
+
+ Returns:
+ A ``SimulationBackend`` instance ready for ``create_world()``.
+
+ Raises:
+ ValueError: If the backend name is not recognized.
+ ImportError: If the backend's dependencies are missing
+ (e.g., ``pip install mujoco``).
+
+ Examples::
+
+ # Default (MuJoCo)
+ sim = create_simulation()
+ sim.create_world()
+ sim.add_robot("so100")
+
+ # With alias
+ sim = create_simulation("mj")
+
+ # Pass kwargs to backend constructor
+ sim = create_simulation("mujoco", tool_name="my_sim")
+ """
+ canonical = _resolve_name(backend)
+ logger.info("Creating simulation: %s (resolved from %r)", canonical, backend)
+
+ BackendClass = _import_backend_class(canonical)
+ return BackendClass(**kwargs)
diff --git a/strands_robots/simulation/model_registry.py b/strands_robots/simulation/model_registry.py
new file mode 100644
index 0000000..6bced4d
--- /dev/null
+++ b/strands_robots/simulation/model_registry.py
@@ -0,0 +1,107 @@
+"""Robot model resolution — URDF registry + Menagerie asset manager."""
+
+import logging
+import os
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# Default URDF search paths (checked in order)
+_URDF_SEARCH_PATHS = [
+ Path.cwd() / "urdfs",
+ Path.cwd() / "assets" / "urdfs",
+ Path.cwd() / "robots",
+ Path.home() / ".strands_robots" / "urdfs",
+]
+
+try:
+ from strands_robots.assets import ( # noqa: I001
+ format_robot_table as _format_robot_table,
+ resolve_model_path as _resolve_menagerie_model,
+ )
+
+ _HAS_ASSET_MANAGER = True
+except ImportError:
+ _HAS_ASSET_MANAGER = False
+
+# Legacy URDF registry — runtime cache for user-registered URDFs
+_URDF_REGISTRY: dict[str, str] = {}
+
+_URDF_DIR_OVERRIDE = os.getenv("STRANDS_URDF_DIR")
+if _URDF_DIR_OVERRIDE:
+ _URDF_SEARCH_PATHS.insert(0, Path(_URDF_DIR_OVERRIDE))
+
+
+def register_urdf(data_config: str, urdf_path: str):
+ """Register a URDF/MJCF file for a data_config name."""
+ _URDF_REGISTRY[data_config] = urdf_path
+ logger.info("📋 Registered model for '%s': %s", data_config, urdf_path)
+
+
+def resolve_model(name: str, prefer_scene: bool = True) -> str | None:
+ """Resolve a robot name or data_config to an MJCF/URDF model path.
+
+ Resolution order:
+ 1. Asset manager (32 bundled robots + 40 aliases)
+ 2. Legacy URDF registry (custom registrations)
+ 3. URDF search paths (STRANDS_URDF_DIR, ./urdfs, etc.)
+ """
+ if _HAS_ASSET_MANAGER:
+ path = _resolve_menagerie_model(name, prefer_scene=prefer_scene)
+ if path and path.exists():
+ return str(path)
+ if prefer_scene:
+ path = _resolve_menagerie_model(name, prefer_scene=False)
+ if path and path.exists():
+ return str(path)
+
+ return resolve_urdf(name)
+
+
+def resolve_urdf(data_config: str) -> str | None:
+ """Resolve a data_config name to a URDF file path (legacy)."""
+ if data_config in _URDF_REGISTRY:
+ urdf_rel = _URDF_REGISTRY[data_config]
+ if os.path.isabs(urdf_rel) and os.path.exists(urdf_rel):
+ return urdf_rel
+ for search_dir in _URDF_SEARCH_PATHS:
+ candidate = search_dir / urdf_rel
+ if candidate.exists():
+ return str(candidate)
+
+ try:
+ from strands_robots.registry import get_robot, resolve_name
+
+ canonical = resolve_name(data_config)
+ info = get_robot(canonical)
+ if info and "legacy_urdf" in info:
+ urdf_rel = info["legacy_urdf"]
+ if os.path.isabs(urdf_rel) and os.path.exists(urdf_rel):
+ return urdf_rel
+ for search_dir in _URDF_SEARCH_PATHS:
+ candidate = search_dir / urdf_rel
+ if candidate.exists():
+ return str(candidate)
+ except ImportError:
+ pass
+
+ logger.debug("URDF not found for '%s' in search paths", data_config)
+ return None
+
+
+def list_registered_urdfs() -> dict[str, str | None]:
+ """List all registered URDF mappings and their resolved paths."""
+ return {config_name: resolve_urdf(config_name) for config_name in _URDF_REGISTRY}
+
+
+def list_available_models() -> str:
+ """List all available robot models (Menagerie + custom)."""
+ if _HAS_ASSET_MANAGER:
+ return _format_robot_table()
+
+ lines = ["Registered URDFs:"]
+ for name, path in _URDF_REGISTRY.items():
+ resolved = resolve_urdf(name)
+ status = "✅" if resolved else "❌"
+ lines.append(f" {status} {name}: {path}")
+ return "\n".join(lines)
diff --git a/strands_robots/simulation/models.py b/strands_robots/simulation/models.py
new file mode 100644
index 0000000..e3a5301
--- /dev/null
+++ b/strands_robots/simulation/models.py
@@ -0,0 +1,108 @@
+"""Dataclasses for simulation state."""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+
+class SimStatus(Enum):
+ """Simulation execution status."""
+
+ IDLE = "idle"
+ RUNNING = "running"
+ PAUSED = "paused"
+ COMPLETED = "completed"
+ ERROR = "error"
+
+
+@dataclass
+class SimRobot:
+ """A robot instance within the simulation."""
+
+ name: str
+ urdf_path: str
+ position: list[float] = field(default_factory=lambda: [0.0, 0.0, 0.0])
+ orientation: list[float] = field(default_factory=lambda: [1.0, 0.0, 0.0, 0.0]) # wxyz quat
+ data_config: str | None = None
+ body_id: int = -1
+ joint_ids: list[int] = field(default_factory=list)
+ joint_names: list[str] = field(default_factory=list)
+ actuator_ids: list[int] = field(default_factory=list)
+ namespace: str = ""
+ policy_running: bool = False
+ policy_steps: int = 0
+ policy_instruction: str = ""
+
+
+@dataclass
+class SimObject:
+ """An object in the simulation scene."""
+
+ name: str
+ shape: str # "box", "sphere", "cylinder", "capsule", "mesh"
+ position: list[float] = field(default_factory=lambda: [0.0, 0.0, 0.0])
+ orientation: list[float] = field(default_factory=lambda: [1.0, 0.0, 0.0, 0.0])
+ size: list[float] = field(default_factory=lambda: [0.05, 0.05, 0.05])
+ color: list[float] = field(default_factory=lambda: [0.5, 0.5, 0.5, 1.0]) # RGBA
+ mass: float = 0.1
+ mesh_path: str | None = None
+ body_id: int = -1
+ is_static: bool = False
+ _original_position: list[float] = field(default_factory=list)
+ _original_color: list[float] = field(default_factory=list)
+
+ def __post_init__(self):
+ self._original_position = list(self.position)
+ self._original_color = list(self.color)
+
+
+@dataclass
+class SimCamera:
+ """A camera in the simulation."""
+
+ name: str
+ position: list[float] = field(default_factory=lambda: [1.0, 1.0, 1.0])
+ target: list[float] = field(default_factory=lambda: [0.0, 0.0, 0.0])
+ fov: float = 60.0
+ width: int = 640
+ height: int = 480
+ camera_id: int = -1
+
+
+@dataclass
+class TrajectoryStep:
+ """A single step in a recorded trajectory."""
+
+ timestamp: float
+ sim_time: float
+ robot_name: str
+ observation: dict[str, Any]
+ action: dict[str, Any]
+ instruction: str = ""
+
+
+@dataclass
+class SimWorld:
+ """Complete simulation world state."""
+
+ robots: dict[str, SimRobot] = field(default_factory=dict)
+ objects: dict[str, SimObject] = field(default_factory=dict)
+ cameras: dict[str, SimCamera] = field(default_factory=dict)
+ timestep: float = 0.002 # 500Hz physics
+ gravity: list[float] = field(default_factory=lambda: [0.0, 0.0, -9.81])
+ ground_plane: bool = True
+ status: SimStatus = SimStatus.IDLE
+ sim_time: float = 0.0
+ step_count: int = 0
+ # MuJoCo internals (set after world is built)
+ _xml: str = ""
+ _model: Any = None
+ _data: Any = None
+ _robot_base_xml: str = ""
+ # Trajectory recording
+ _recording: bool = False
+ _trajectory: list[TrajectoryStep] = field(default_factory=list)
+ # LeRobotDataset recorder
+ _dataset_recorder: Any = None
+ # Temp directory for scene composition
+ _tmpdir: Any = None
diff --git a/strands_robots/simulation/mujoco/__init__.py b/strands_robots/simulation/mujoco/__init__.py
new file mode 100644
index 0000000..014926b
--- /dev/null
+++ b/strands_robots/simulation/mujoco/__init__.py
@@ -0,0 +1,41 @@
+"""MuJoCo simulation backend for strands-robots.
+
+CPU-based physics with offscreen rendering. No GPU required.
+Supports URDF/MJCF loading, multi-robot scenes, policy execution,
+domain randomization, and LeRobotDataset recording.
+
+Usage::
+
+ from strands_robots.simulation.mujoco import MuJoCoSimulation
+
+ sim = MuJoCoSimulation(tool_name="my_sim")
+ sim.create_world()
+ sim.add_robot("so100", data_config="so100")
+ sim.run_policy("so100", policy_provider="mock", instruction="wave")
+
+Or via the top-level alias::
+
+ from strands_robots.simulation import Simulation # → MuJoCoSimulation
+"""
+
+from strands_robots.simulation.mujoco.backend import (
+ _configure_gl_backend,
+ _ensure_mujoco,
+ _is_headless,
+)
+
+__all__ = [
+ "MuJoCoSimulation",
+ "_configure_gl_backend",
+ "_ensure_mujoco",
+ "_is_headless",
+]
+
+
+def __getattr__(name):
+ if name == "MuJoCoSimulation":
+ from strands_robots.simulation.mujoco.simulation import Simulation as _Sim
+
+ globals()["MuJoCoSimulation"] = _Sim
+ return _Sim
+ raise AttributeError(f"module 'strands_robots.simulation.mujoco' has no attribute {name!r}")
diff --git a/strands_robots/simulation/mujoco/backend.py b/strands_robots/simulation/mujoco/backend.py
new file mode 100644
index 0000000..da9a268
--- /dev/null
+++ b/strands_robots/simulation/mujoco/backend.py
@@ -0,0 +1,132 @@
+"""MuJoCo lazy import and GL backend configuration."""
+
+import ctypes
+import logging
+import os
+import sys
+
+logger = logging.getLogger(__name__)
+
+_mujoco = None
+_mujoco_viewer = None
+
+
+def _is_headless() -> bool:
+ """Detect if running in a headless environment (no display server).
+
+ Returns True on Linux when no DISPLAY or WAYLAND_DISPLAY is set,
+ which means GLFW-based rendering will fail.
+ """
+ if sys.platform != "linux":
+ return False
+ if os.environ.get("DISPLAY") or os.environ.get("WAYLAND_DISPLAY"):
+ return False
+ return True
+
+
+def _configure_gl_backend() -> None:
+ """Auto-configure MuJoCo's OpenGL backend for headless environments.
+
+ MuJoCo reads MUJOCO_GL at import time to select the OpenGL backend:
+ - "egl" → EGL (GPU-accelerated offscreen, requires libEGL + NVIDIA driver)
+ - "osmesa" → OSMesa (CPU software rendering, slower but always works)
+ - "glfw" → GLFW (default, requires X11/Wayland display server)
+
+ This function MUST be called before `import mujoco`. Setting MUJOCO_GL
+ after import has no effect — the backend is locked at import time.
+
+ Never overrides a user-set MUJOCO_GL value.
+ """
+ if os.environ.get("MUJOCO_GL"):
+ logger.debug(f"MUJOCO_GL already set to '{os.environ['MUJOCO_GL']}', respecting user config")
+ return
+
+ if not _is_headless():
+ return
+
+ # Headless Linux — probe for EGL first (GPU-accelerated), then fall back to OSMesa (CPU)
+ try:
+ ctypes.cdll.LoadLibrary("libEGL.so.1")
+ os.environ["MUJOCO_GL"] = "egl"
+ logger.info("Headless environment detected — using MUJOCO_GL=egl (GPU-accelerated offscreen)")
+ return
+ except OSError:
+ pass
+
+ try:
+ ctypes.cdll.LoadLibrary("libOSMesa.so")
+ os.environ["MUJOCO_GL"] = "osmesa"
+ logger.info("Headless environment detected — using MUJOCO_GL=osmesa (CPU software rendering)")
+ return
+ except OSError:
+ pass
+
+ logger.warning(
+ "Headless environment detected but neither EGL nor OSMesa found. "
+ "MuJoCo rendering will likely fail. Install one of:\n"
+ " GPU: apt-get install libegl1-mesa-dev (or NVIDIA driver provides libEGL)\n"
+ " CPU: apt-get install libosmesa6-dev\n"
+ "Then set: export MUJOCO_GL=egl (or osmesa)"
+ )
+
+
+def _ensure_mujoco():
+ """Lazy import MuJoCo to avoid hard dependency.
+
+ Auto-configures the OpenGL backend for headless environments before
+ importing mujoco, since MUJOCO_GL must be set at import time.
+
+ Uses require_optional() for consistent dependency management across
+ the strands-robots package.
+ """
+ global _mujoco, _mujoco_viewer
+ if _mujoco is None:
+ _configure_gl_backend()
+ from strands_robots.utils import require_optional
+
+ _mujoco = require_optional(
+ "mujoco",
+ pip_install="mujoco",
+ extra="sim",
+ purpose="MuJoCo simulation",
+ )
+ if _mujoco_viewer is None and not _is_headless():
+ try:
+ import mujoco.viewer as viewer
+
+ _mujoco_viewer = viewer
+ except ImportError:
+ pass
+ return _mujoco
+
+
+_rendering_available: bool | None = None
+
+
+def _can_render() -> bool:
+ """Check if MuJoCo offscreen rendering is available.
+
+ Probes once by creating a minimal Renderer. Result is cached.
+ Returns False on headless environments without EGL/OSMesa.
+ """
+ global _rendering_available
+ if _rendering_available is not None:
+ return _rendering_available
+
+ mj = _ensure_mujoco()
+ try:
+ model = mj.MjModel.from_xml_string("
")
+ renderer = mj.Renderer(model, height=1, width=1)
+ renderer.close()
+ del renderer
+ _rendering_available = True
+ logger.info("MuJoCo rendering available")
+ except Exception as e:
+ _rendering_available = False
+ logger.warning(
+ "MuJoCo rendering unavailable: %s. "
+ "Physics/policy will work, but render/camera observations will be skipped. "
+ "Install EGL or OSMesa for offscreen rendering.",
+ e,
+ )
+ return _rendering_available
diff --git a/strands_robots/simulation/mujoco/mjcf_builder.py b/strands_robots/simulation/mujoco/mjcf_builder.py
new file mode 100644
index 0000000..5dcdc69
--- /dev/null
+++ b/strands_robots/simulation/mujoco/mjcf_builder.py
@@ -0,0 +1,197 @@
+"""MJCF XML builder — programmatic scene construction."""
+
+import logging
+import os
+import subprocess
+import tempfile
+
+from strands_robots.simulation.models import SimCamera, SimObject, SimRobot, SimWorld
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+
+logger = logging.getLogger(__name__)
+
+
+class MJCFBuilder:
+ """Builds MuJoCo MJCF XML from SimWorld state."""
+
+ @staticmethod
+ def build_objects_only(world: SimWorld) -> str:
+ """Build MJCF XML for a world with only objects (robots loaded separately)."""
+ _ensure_mujoco()
+
+ parts = []
+ parts.append('
')
+ parts.append(' ')
+
+ gx, gy, gz = world.gravity
+ parts.append(f' ')
+
+ parts.append(" ")
+ parts.append(' ')
+ parts.append(' ')
+ parts.append(" ")
+
+ parts.append(" ")
+ parts.append(
+ ' '
+ )
+ parts.append(' ')
+ for obj in world.objects.values():
+ if obj.shape == "mesh" and obj.mesh_path:
+ parts.append(f' ')
+ parts.append(" ")
+
+ parts.append(" ")
+ parts.append(' ')
+ parts.append(' ')
+
+ if world.ground_plane:
+ parts.append(
+ ' '
+ )
+
+ for cam in world.cameras.values():
+ px, py, pz = cam.position
+ parts.append(f' ')
+
+ for obj in world.objects.values():
+ parts.append(MJCFBuilder._object_xml(obj, indent=4))
+
+ parts.append(" ")
+ parts.append("")
+
+ return "\n".join(parts)
+
+ @staticmethod
+ def _object_xml(obj: SimObject, indent: int = 4) -> str:
+ """Generate MJCF XML for a single object."""
+ pad = " " * indent
+ px, py, pz = obj.position
+ qw, qx, qy, qz = obj.orientation
+ r, g, b, a = obj.color
+ lines = []
+
+ lines.append(f'{pad}')
+
+ if not obj.is_static:
+ lines.append(f'{pad}
')
+ lines.append(f'{pad}
')
+
+ if obj.shape == "box":
+ sx, sy, sz = [s / 2 for s in obj.size]
+ lines.append(
+ f'{pad}
'
+ )
+ elif obj.shape == "sphere":
+ radius = obj.size[0] / 2 if obj.size else 0.025
+ lines.append(
+ f'{pad}
'
+ )
+ elif obj.shape == "cylinder":
+ radius = obj.size[0] / 2 if obj.size else 0.025
+ half_h = obj.size[2] / 2 if len(obj.size) > 2 else 0.05
+ lines.append(
+ f'{pad}
'
+ )
+ elif obj.shape == "capsule":
+ radius = obj.size[0] / 2 if obj.size else 0.025
+ half_h = obj.size[2] / 2 if len(obj.size) > 2 else 0.05
+ lines.append(
+ f'{pad}
'
+ )
+ elif obj.shape == "mesh" and obj.mesh_path:
+ lines.append(
+ f'{pad}
'
+ )
+ elif obj.shape == "plane":
+ sx = obj.size[0] if obj.size else 1.0
+ sy = obj.size[1] if len(obj.size) > 1 else sx
+ lines.append(
+ f'{pad}
'
+ )
+
+ lines.append(f"{pad}")
+ return "\n".join(lines)
+
+ @staticmethod
+ def compose_multi_robot_scene(
+ robots: dict[str, SimRobot],
+ objects: dict[str, SimObject],
+ cameras: dict[str, SimCamera],
+ world: SimWorld,
+ ) -> str:
+ """Compose a multi-robot scene by merging URDF-derived MJCF fragments."""
+ mj = _ensure_mujoco()
+ world._tmpdir = tempfile.TemporaryDirectory(prefix="strands_sim_")
+ tmpdir = world._tmpdir.name
+
+ robot_xmls = {}
+ for robot_name, robot in robots.items():
+ try:
+ model = mj.MjModel.from_xml_path(str(robot.urdf_path))
+ robot_xml_path = os.path.join(tmpdir, f"{robot_name}.xml")
+ mj.mj_saveLastXML(robot_xml_path, model)
+ robot_xmls[robot_name] = robot_xml_path
+ logger.debug("Converted %s → %s", robot.urdf_path, robot_xml_path)
+ except (FileNotFoundError, OSError, subprocess.CalledProcessError) as e:
+ logger.error("Failed to convert URDF for '%s': %s", robot_name, e)
+ raise
+
+ parts = []
+ parts.append('
')
+ parts.append(' ')
+
+ gx, gy, gz = world.gravity
+ parts.append(f' ')
+
+ parts.append(" ")
+ parts.append(' ')
+ parts.append(' ')
+ parts.append(" ")
+
+ parts.append(" ")
+ parts.append(
+ ' '
+ )
+ parts.append(' ')
+ for obj in objects.values():
+ if obj.shape == "mesh" and obj.mesh_path:
+ parts.append(f' ')
+ parts.append(" ")
+
+ parts.append(" ")
+ parts.append(' ')
+ parts.append(' ')
+
+ if world.ground_plane:
+ parts.append(
+ ' '
+ )
+
+ for cam in cameras.values():
+ px, py, pz = cam.position
+ parts.append(f' ')
+
+ for robot_name, robot in robots.items():
+ xml_path = robot_xmls[robot_name]
+ parts.append(f" ")
+ parts.append(f' ')
+
+ for obj in objects.values():
+ parts.append(MJCFBuilder._object_xml(obj, indent=4))
+
+ parts.append(" ")
+ parts.append("")
+
+ master_xml = "\n".join(parts)
+ master_path = os.path.join(tmpdir, "master_scene.xml")
+ with open(master_path, "w") as f:
+ f.write(master_xml)
+
+ return master_path
diff --git a/strands_robots/simulation/mujoco/physics.py b/strands_robots/simulation/mujoco/physics.py
new file mode 100644
index 0000000..1afc7e8
--- /dev/null
+++ b/strands_robots/simulation/mujoco/physics.py
@@ -0,0 +1,821 @@
+"""Physics mixin — advanced MuJoCo physics introspection and manipulation.
+
+Exposes the deep MuJoCo C API through clean Python methods:
+- Raycasting (mj_ray)
+- Jacobians (mj_jacBody, mj_jacSite, mj_jacGeom)
+- Energy computation (mj_energyPos, mj_energyVel)
+- External forces (mj_applyFT, xfrc_applied)
+- Mass matrix (mj_fullM)
+- State checkpointing (mj_getState, mj_setState)
+- Inverse dynamics (mj_inverse)
+- Body/joint introspection (poses, velocities, accelerations)
+- Direct joint position/velocity control (qpos, qvel)
+- Runtime model modification (mass, friction, color, size)
+- Sensor readout (sensordata)
+- Contact force analysis (mj_contactForce)
+"""
+
+import json
+import logging
+from typing import Any
+
+import numpy as np
+
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+
+logger = logging.getLogger(__name__)
+
+
+class PhysicsMixin:
+ """Advanced physics capabilities for Simulation.
+
+ Expects: self._world (SimWorld with _model, _data)
+
+ Naming: methods match action names in tool_spec.json for direct dispatch.
+ """
+
+ # ── State Checkpointing ──
+
+ def save_state(self, name: str = "default") -> dict[str, Any]:
+ """Save the full physics state (qpos, qvel, act, time) to a named checkpoint.
+
+ Uses mj_getState with mjSTATE_PHYSICS for complete state capture.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ state_size = mj.mj_stateSize(model, mj.mjtState.mjSTATE_PHYSICS)
+ state = np.zeros(state_size)
+ mj.mj_getState(model, data, state, mj.mjtState.mjSTATE_PHYSICS)
+
+ if not hasattr(self._world, "_checkpoints"):
+ self._world._checkpoints = {}
+
+ self._world._checkpoints[name] = {
+ "state": state.copy(),
+ "sim_time": self._world.sim_time,
+ "step_count": self._world.step_count,
+ }
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"💾 State '{name}' saved\n"
+ f" t={self._world.sim_time:.4f}s, step={self._world.step_count}\n"
+ f" State vector: {state_size} floats\n"
+ f" Checkpoints: {list(self._world._checkpoints.keys())}"
+ )
+ }
+ ],
+ }
+
+ def load_state(self, name: str = "default") -> dict[str, Any]:
+ """Restore physics state from a named checkpoint."""
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ checkpoints = getattr(self._world, "_checkpoints", {})
+ if name not in checkpoints:
+ available = list(checkpoints.keys()) if checkpoints else ["none"]
+ return {
+ "status": "error",
+ "content": [{"text": f"❌ Checkpoint '{name}' not found. Available: {available}"}],
+ }
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+ checkpoint = checkpoints[name]
+
+ mj.mj_setState(model, data, checkpoint["state"], mj.mjtState.mjSTATE_PHYSICS)
+ mj.mj_forward(model, data)
+
+ self._world.sim_time = checkpoint["sim_time"]
+ self._world.step_count = checkpoint["step_count"]
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"📂 State '{name}' restored (t={self._world.sim_time:.4f}s, step={self._world.step_count})"}
+ ],
+ }
+
+ # ── External Forces ──
+
+ def apply_force(
+ self,
+ body_name: str,
+ force: list[float] = None,
+ torque: list[float] = None,
+ point: list[float] = None,
+ ) -> dict[str, Any]:
+ """Apply external force and/or torque to a body.
+
+ Uses mj_applyFT for precise force application at a world-frame point.
+ Forces persist for one timestep — call before each step for continuous force.
+
+ Args:
+ body_name: Target body name.
+ force: [fx, fy, fz] in world frame (Newtons).
+ torque: [tx, ty, tz] in world frame (N·m).
+ point: [px, py, pz] world-frame point of force application.
+ Defaults to body CoM if not specified.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ body_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_BODY, body_name)
+ if body_id < 0:
+ return {"status": "error", "content": [{"text": f"❌ Body '{body_name}' not found."}]}
+
+ f = np.array(force or [0, 0, 0], dtype=np.float64)
+ t = np.array(torque or [0, 0, 0], dtype=np.float64)
+ p = np.array(point, dtype=np.float64) if point else data.xipos[body_id].copy()
+
+ mj.mj_applyFT(model, data, f, t, p, body_id, data.qfrc_applied)
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"💨 Force applied to '{body_name}' (body {body_id})\n"
+ f" Force: {f.tolist()} N\n"
+ f" Torque: {t.tolist()} N·m\n"
+ f" Point: {p.tolist()}"
+ )
+ }
+ ],
+ }
+
+ # ── Raycasting ──
+
+ def raycast(
+ self,
+ origin: list[float],
+ direction: list[float],
+ exclude_body: int = -1,
+ include_static: bool = True,
+ ) -> dict[str, Any]:
+ """Cast a ray and find the first geom intersection.
+
+ Uses mj_ray for precise distance sensing / obstacle detection.
+
+ Args:
+ origin: [x, y, z] ray start point in world frame.
+ direction: [dx, dy, dz] ray direction (auto-normalized).
+ exclude_body: Body ID to exclude from intersection (-1 = none).
+ include_static: Whether to include static geoms.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ pnt = np.array(origin, dtype=np.float64)
+ vec = np.array(direction, dtype=np.float64)
+ # Normalize direction
+ norm = np.linalg.norm(vec)
+ if norm > 0:
+ vec = vec / norm
+
+ geomid = np.array([-1], dtype=np.int32)
+ dist = mj.mj_ray(
+ model,
+ data,
+ pnt,
+ vec,
+ None, # geom group filter (None = all)
+ 1 if include_static else 0,
+ exclude_body,
+ geomid,
+ )
+
+ hit = dist >= 0
+ geom_name = None
+ if hit and geomid[0] >= 0:
+ geom_name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_GEOM, geomid[0])
+
+ result = {
+ "hit": hit,
+ "distance": float(dist) if hit else None,
+ "geom_id": int(geomid[0]) if hit else None,
+ "geom_name": geom_name,
+ "hit_point": (pnt + vec * dist).tolist() if hit else None,
+ }
+
+ if hit:
+ text = f"🎯 Ray hit '{geom_name or geomid[0]}' at dist={dist:.4f}m, point={result['hit_point']}"
+ else:
+ text = "🎯 Ray: no intersection"
+
+ return {"status": "success", "content": [{"text": text}, {"text": json.dumps(result, default=str)}]}
+
+ # ── Jacobians ──
+
+ def get_jacobian(
+ self,
+ body_name: str = None,
+ site_name: str = None,
+ geom_name: str = None,
+ ) -> dict[str, Any]:
+ """Compute the Jacobian (position + rotation) for a body, site, or geom.
+
+ The Jacobian maps joint velocities to Cartesian velocities:
+ v = J @ dq
+
+ Returns both positional (3×nv) and rotational (3×nv) Jacobians.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ jacp = np.zeros((3, model.nv))
+ jacr = np.zeros((3, model.nv))
+
+ if body_name:
+ obj_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_BODY, body_name)
+ if obj_id < 0:
+ return {"status": "error", "content": [{"text": f"❌ Body '{body_name}' not found."}]}
+ mj.mj_jacBody(model, data, jacp, jacr, obj_id)
+ label = f"body '{body_name}'"
+ elif site_name:
+ obj_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_SITE, site_name)
+ if obj_id < 0:
+ return {"status": "error", "content": [{"text": f"❌ Site '{site_name}' not found."}]}
+ mj.mj_jacSite(model, data, jacp, jacr, obj_id)
+ label = f"site '{site_name}'"
+ elif geom_name:
+ obj_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_GEOM, geom_name)
+ if obj_id < 0:
+ return {"status": "error", "content": [{"text": f"❌ Geom '{geom_name}' not found."}]}
+ mj.mj_jacGeom(model, data, jacp, jacr, obj_id)
+ label = f"geom '{geom_name}'"
+ else:
+ return {"status": "error", "content": [{"text": "❌ Specify body_name, site_name, or geom_name."}]}
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"🧮 Jacobian for {label}: pos={jacp.shape}, rot={jacr.shape}, nv={model.nv}"},
+ {
+ "text": json.dumps(
+ {
+ "jacp": jacp.tolist(),
+ "jacr": jacr.tolist(),
+ "nv": model.nv,
+ },
+ default=str,
+ )
+ },
+ ],
+ }
+
+ # ── Energy ──
+
+ def get_energy(self) -> dict[str, Any]:
+ """Compute potential and kinetic energy of the system."""
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ mj.mj_energyPos(model, data)
+ mj.mj_energyVel(model, data)
+
+ potential = float(data.energy[0])
+ kinetic = float(data.energy[1])
+ total = potential + kinetic
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"⚡ Energy: potential={potential:.4f}J, kinetic={kinetic:.4f}J, total={total:.4f}J"},
+ {"text": json.dumps({"potential": potential, "kinetic": kinetic, "total": total}, default=str)},
+ ],
+ }
+
+ # ── Mass Matrix ──
+
+ def get_mass_matrix(self) -> dict[str, Any]:
+ """Compute the full mass (inertia) matrix M(q).
+
+ M is nv×nv where nv is the number of DoFs.
+ Useful for dynamics analysis, impedance control, etc.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ nv = model.nv
+ M = np.zeros((nv, nv))
+ mj.mj_fullM(model, M, data.qM)
+ rank = int(np.linalg.matrix_rank(M))
+ cond = float(np.linalg.cond(M)) if rank > 0 else float("inf")
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"🧮 Mass matrix: {nv}×{nv}, rank={rank}, cond={cond:.2e}"},
+ {
+ "text": json.dumps(
+ {
+ "shape": [nv, nv],
+ "rank": rank,
+ "condition_number": cond,
+ "diagonal": np.diag(M).tolist(),
+ "total_mass": float(np.sum(model.body_mass)),
+ },
+ default=str,
+ )
+ },
+ ],
+ }
+
+ # ── Inverse Dynamics ──
+
+ def inverse_dynamics(self) -> dict[str, Any]:
+ """Compute inverse dynamics: given qacc, what forces are needed?
+
+ Runs mj_inverse to compute qfrc_inverse — the generalized forces
+ that would produce the current accelerations.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ mj.mj_inverse(model, data)
+
+ # Build named force mapping
+ forces = {}
+ for i in range(model.njnt):
+ name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_JOINT, i)
+ if name:
+ dof_adr = model.jnt_dofadr[i]
+ forces[name] = float(data.qfrc_inverse[dof_adr])
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"🔄 Inverse dynamics: {len(forces)} joint forces computed"},
+ {"text": json.dumps({"qfrc_inverse": forces}, default=str)},
+ ],
+ }
+
+ # ── Body Introspection ──
+
+ def get_body_state(
+ self,
+ body_name: str,
+ ) -> dict[str, Any]:
+ """Get the full state of a body: position, orientation, velocity, acceleration.
+
+ Returns Cartesian pose + 6D spatial velocity (linear + angular).
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ body_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_BODY, body_name)
+ if body_id < 0:
+ return {"status": "error", "content": [{"text": f"❌ Body '{body_name}' not found."}]}
+
+ # Position and orientation
+ pos = data.xpos[body_id].tolist()
+ quat = data.xquat[body_id].tolist()
+ rotmat = data.xmat[body_id].reshape(3, 3).tolist()
+
+ # Velocity (6D: angular then linear in world frame)
+ vel = np.zeros(6)
+ mj.mj_objectVelocity(model, data, mj.mjtObj.mjOBJ_BODY, body_id, vel, 0)
+ linvel = vel[3:].tolist()
+ angvel = vel[:3].tolist()
+
+ # Mass and inertia
+ mass = float(model.body_mass[body_id])
+ com = data.xipos[body_id].tolist()
+
+ state = {
+ "position": pos,
+ "quaternion": quat,
+ "rotation_matrix": rotmat,
+ "linear_velocity": linvel,
+ "angular_velocity": angvel,
+ "mass": mass,
+ "center_of_mass": com,
+ }
+
+ text = (
+ f"🏷️ Body '{body_name}' (id={body_id}):\n"
+ f" pos: [{pos[0]:.4f}, {pos[1]:.4f}, {pos[2]:.4f}]\n"
+ f" quat: [{quat[0]:.4f}, {quat[1]:.4f}, {quat[2]:.4f}, {quat[3]:.4f}]\n"
+ f" linvel: [{linvel[0]:.4f}, {linvel[1]:.4f}, {linvel[2]:.4f}]\n"
+ f" angvel: [{angvel[0]:.4f}, {angvel[1]:.4f}, {angvel[2]:.4f}]\n"
+ f" mass: {mass:.4f}kg, com: {com}"
+ )
+
+ return {"status": "success", "content": [{"text": text}, {"text": json.dumps(state, default=str)}]}
+
+ # ── Direct Joint Control ──
+
+ def set_joint_positions(
+ self,
+ positions: dict[str, float] = None,
+ robot_name: str = None,
+ ) -> dict[str, Any]:
+ """Set joint positions directly (bypassing actuators).
+
+ Writes to qpos and runs mj_forward to update kinematics.
+ Useful for teleportation, IK solutions, or keyframe setting.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ if positions is None:
+ return {"status": "error", "content": [{"text": "❌ positions dict required."}]}
+
+ set_count = 0
+ for jnt_name, value in positions.items():
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, jnt_name)
+ if jnt_id >= 0:
+ qpos_adr = model.jnt_qposadr[jnt_id]
+ data.qpos[qpos_adr] = float(value)
+ set_count += 1
+ else:
+ logger.warning("Joint '%s' not found, skipping", jnt_name)
+
+ mj.mj_forward(model, data)
+
+ return {
+ "status": "success",
+ "content": [{"text": f"🎯 Set {set_count}/{len(positions)} joint positions, FK updated"}],
+ }
+
+ def set_joint_velocities(
+ self,
+ velocities: dict[str, float] = None,
+ ) -> dict[str, Any]:
+ """Set joint velocities directly.
+
+ Writes to qvel. Useful for initializing dynamics.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ if velocities is None:
+ return {"status": "error", "content": [{"text": "❌ velocities dict required."}]}
+
+ set_count = 0
+ for jnt_name, value in velocities.items():
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, jnt_name)
+ if jnt_id >= 0:
+ dof_adr = model.jnt_dofadr[jnt_id]
+ data.qvel[dof_adr] = float(value)
+ set_count += 1
+
+ return {
+ "status": "success",
+ "content": [{"text": f"💨 Set {set_count}/{len(velocities)} joint velocities"}],
+ }
+
+ # ── Sensor Readout ──
+
+ def get_sensor_data(self, sensor_name: str = None) -> dict[str, Any]:
+ """Read sensor values from the simulation.
+
+ MuJoCo supports: jointpos, jointvel, accelerometer, gyro, force,
+ torque, touch, rangefinder, framequat, subtreecom, clock, etc.
+
+ Args:
+ sensor_name: Specific sensor name, or None for all sensors.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ if model.nsensor == 0:
+ return {"status": "success", "content": [{"text": "📡 No sensors in model."}]}
+
+ mj.mj_forward(model, data)
+
+ sensors = {}
+ for i in range(model.nsensor):
+ name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_SENSOR, i)
+ if not name:
+ name = f"sensor_{i}"
+
+ adr = model.sensor_adr[i]
+ dim = model.sensor_dim[i]
+ values = data.sensordata[adr : adr + dim].tolist()
+
+ if sensor_name and name != sensor_name:
+ continue
+
+ sensors[name] = {
+ "values": values if dim > 1 else values[0],
+ "dim": int(dim),
+ "type": int(model.sensor_type[i]),
+ }
+
+ if sensor_name and sensor_name not in sensors:
+ return {"status": "error", "content": [{"text": f"❌ Sensor '{sensor_name}' not found."}]}
+
+ lines = [f"📡 Sensors ({len(sensors)}/{model.nsensor}):"]
+ for name, info in sensors.items():
+ lines.append(f" {name}: {info['values']} (dim={info['dim']})")
+
+ return {
+ "status": "success",
+ "content": [{"text": "\n".join(lines)}, {"text": json.dumps({"sensors": sensors}, default=str)}],
+ }
+
+ # ── Runtime Model Modification ──
+
+ def set_body_properties(
+ self,
+ body_name: str,
+ mass: float = None,
+ ) -> dict[str, Any]:
+ """Modify body properties at runtime (no recompile needed).
+
+ Changes take effect on the next mj_step.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model = self._world._model
+ body_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_BODY, body_name)
+ if body_id < 0:
+ return {"status": "error", "content": [{"text": f"❌ Body '{body_name}' not found."}]}
+
+ changes = []
+ if mass is not None:
+ old_mass = float(model.body_mass[body_id])
+ model.body_mass[body_id] = mass
+ changes.append(f"mass: {old_mass:.3f} → {mass:.3f}")
+
+ return {
+ "status": "success",
+ "content": [{"text": f"🔧 Body '{body_name}': {', '.join(changes)}"}],
+ }
+
+ def set_geom_properties(
+ self,
+ geom_name: str = None,
+ geom_id: int = None,
+ color: list[float] = None,
+ friction: list[float] = None,
+ size: list[float] = None,
+ ) -> dict[str, Any]:
+ """Modify geom properties at runtime (no recompile needed).
+
+ Changes take effect immediately for rendering (color) or next step (friction, size).
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model = self._world._model
+
+ gid = geom_id
+ if geom_name:
+ gid = mj.mj_name2id(model, mj.mjtObj.mjOBJ_GEOM, geom_name)
+ if gid is None or gid < 0:
+ return {"status": "error", "content": [{"text": f"❌ Geom '{geom_name or geom_id}' not found."}]}
+
+ label = geom_name or f"geom_{gid}"
+ changes = []
+
+ if color is not None:
+ model.geom_rgba[gid] = color[:4] if len(color) >= 4 else color[:3] + [1.0]
+ changes.append(f"color → {model.geom_rgba[gid].tolist()}")
+
+ if friction is not None:
+ fric = friction[:3] if len(friction) >= 3 else friction + [0.0] * (3 - len(friction))
+ model.geom_friction[gid] = fric
+ changes.append(f"friction → {fric}")
+
+ if size is not None:
+ n = min(len(size), 3)
+ model.geom_size[gid, :n] = size[:n]
+ changes.append(f"size → {model.geom_size[gid].tolist()}")
+
+ return {
+ "status": "success",
+ "content": [{"text": f"🔧 Geom '{label}': {', '.join(changes)}"}],
+ }
+
+ # ── Contact Force Analysis ──
+
+ def get_contact_forces(self) -> dict[str, Any]:
+ """Get detailed contact forces for all active contacts.
+
+ Uses mj_contactForce for each active contact pair.
+ Returns normal and friction forces.
+ """
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ contacts = []
+ for i in range(data.ncon):
+ c = data.contact[i]
+ g1 = mj.mj_id2name(model, mj.mjtObj.mjOBJ_GEOM, c.geom1) or f"geom_{c.geom1}"
+ g2 = mj.mj_id2name(model, mj.mjtObj.mjOBJ_GEOM, c.geom2) or f"geom_{c.geom2}"
+
+ # Get contact force (normal + friction in contact frame)
+ force = np.zeros(6)
+ mj.mj_contactForce(model, data, i, force)
+
+ contacts.append(
+ {
+ "geom1": g1,
+ "geom2": g2,
+ "distance": float(c.dist),
+ "position": c.pos.tolist(),
+ "normal_force": float(force[0]),
+ "friction_force": force[1:3].tolist(),
+ "full_wrench": force.tolist(),
+ }
+ )
+
+ if not contacts:
+ return {"status": "success", "content": [{"text": "💥 No active contacts."}]}
+
+ lines = [f"💥 {len(contacts)} contacts:"]
+ for c in contacts[:15]:
+ lines.append(f" {c['geom1']} ↔ {c['geom2']}: normal={c['normal_force']:.3f}N, dist={c['distance']:.4f}m")
+ if len(contacts) > 15:
+ lines.append(f" ... and {len(contacts) - 15} more")
+
+ return {
+ "status": "success",
+ "content": [{"text": "\n".join(lines)}, {"text": json.dumps({"contacts": contacts}, default=str)}],
+ }
+
+ # ── Multi-Ray (batch raycasting) ──
+
+ def multi_raycast(
+ self,
+ origin: list[float],
+ directions: list[list[float]],
+ exclude_body: int = -1,
+ ) -> dict[str, Any]:
+ """Cast multiple rays from a single origin (e.g., for LIDAR simulation).
+
+ Efficiently casts N rays using individual mj_ray calls.
+ Returns array of distances and hit geoms.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ pnt = np.array(origin, dtype=np.float64)
+ results = []
+
+ for d in directions:
+ vec = np.array(d, dtype=np.float64)
+ norm = np.linalg.norm(vec)
+ if norm > 0:
+ vec /= norm
+ geomid = np.array([-1], dtype=np.int32)
+ dist = mj.mj_ray(model, data, pnt, vec, None, 1, exclude_body, geomid)
+ results.append(
+ {
+ "distance": float(dist) if dist >= 0 else None,
+ "geom_id": int(geomid[0]) if dist >= 0 else None,
+ }
+ )
+
+ hit_count = sum(1 for r in results if r["distance"] is not None)
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"🎯 Multi-ray: {hit_count}/{len(directions)} hits from {origin}"},
+ {"text": json.dumps({"rays": results}, default=str)},
+ ],
+ }
+
+ # ── Forward Kinematics (explicit) ──
+
+ def forward_kinematics(self) -> dict[str, Any]:
+ """Run forward kinematics to update all body positions/orientations.
+
+ Usually called implicitly by mj_step, but useful after manually
+ setting qpos to see updated Cartesian positions.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ mj.mj_kinematics(model, data)
+ mj.mj_comPos(model, data)
+ mj.mj_camlight(model, data)
+
+ # Build body position summary
+ bodies = {}
+ for i in range(model.nbody):
+ name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_BODY, i) or f"body_{i}"
+ bodies[name] = {
+ "position": data.xpos[i].tolist(),
+ "quaternion": data.xquat[i].tolist(),
+ }
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"🦴 FK computed for {model.nbody} bodies"},
+ {"text": json.dumps({"bodies": bodies}, default=str)},
+ ],
+ }
+
+ # ── Total Mass ──
+
+ def get_total_mass(self) -> dict[str, Any]:
+ """Get total mass and per-body mass breakdown."""
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model = self._world._model
+
+ total = float(mj.mj_getTotalmass(model))
+ bodies = {}
+ for i in range(model.nbody):
+ name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_BODY, i) or f"body_{i}"
+ m = float(model.body_mass[i])
+ if m > 0:
+ bodies[name] = m
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"⚖️ Total mass: {total:.4f}kg ({len(bodies)} bodies with mass)"},
+ {"text": json.dumps({"total_mass": total, "bodies": bodies}, default=str)},
+ ],
+ }
+
+ # ── Export Model XML ──
+
+ def export_xml(self, output_path: str = None) -> dict[str, Any]:
+ """Export the current model to MJCF XML.
+
+ Uses mj_saveLastXML — exports the exact model currently loaded,
+ including any runtime modifications.
+ """
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+
+ if output_path:
+ mj.mj_saveLastXML(output_path, self._world._model)
+ return {"status": "success", "content": [{"text": f"📄 Model exported to {output_path}"}]}
+ else:
+ # Return XML string via saveLastXML to temp file
+ import os
+ import tempfile
+
+ tmpfile = tempfile.mktemp(suffix=".xml")
+ mj.mj_saveLastXML(tmpfile, self._world._model)
+ with open(tmpfile) as f:
+ xml = f.read()
+ os.unlink(tmpfile)
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"📄 Model XML ({len(xml)} chars):\n{xml[:2000]}{'...' if len(xml) > 2000 else ''}"}
+ ],
+ }
diff --git a/strands_robots/simulation/mujoco/policy_runner.py b/strands_robots/simulation/mujoco/policy_runner.py
new file mode 100644
index 0000000..59c3f8d
--- /dev/null
+++ b/strands_robots/simulation/mujoco/policy_runner.py
@@ -0,0 +1,356 @@
+"""Policy execution mixin — run_policy, start_policy, record_video, replay_episode, eval_policy."""
+
+import logging
+import os
+import time
+from typing import Any
+
+import numpy as np
+
+from strands_robots._async_utils import _resolve_coroutine
+from strands_robots.simulation.models import TrajectoryStep
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+
+logger = logging.getLogger(__name__)
+
+
+class PolicyRunnerMixin:
+ """Policy execution for Simulation. Expects self._world, self._executor, self._policy_threads."""
+
+ def run_policy(
+ self,
+ robot_name: str,
+ policy_provider: str = "mock",
+ instruction: str = "",
+ duration: float = 10.0,
+ action_horizon: int = 8,
+ control_frequency: float = 50.0,
+ fast_mode: bool = False,
+ record_video: str = None,
+ video_fps: int = 30,
+ video_camera: str = None,
+ video_width: int = 640,
+ video_height: int = 480,
+ **policy_kwargs,
+ ) -> dict[str, Any]:
+ """Run a policy on a simulated robot (blocking).
+
+ Args:
+ record_video: If set, path to save an MP4 recording of the run.
+ video_fps: Frames per second for the recording (default 30).
+ video_camera: Camera name for recording (default: first scene camera).
+ video_width: Recording width in pixels.
+ video_height: Recording height in pixels.
+ """
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+ if robot_name not in self._world.robots:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{robot_name}' not found."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+ robot = self._world.robots[robot_name]
+
+ # Video recording setup
+ writer = None
+ frame_count = 0
+ cam_id = -1
+ if record_video:
+ import imageio
+
+ os.makedirs(os.path.dirname(os.path.abspath(record_video)), exist_ok=True)
+ writer = imageio.get_writer(record_video, fps=video_fps, quality=8, macro_block_size=1)
+ if video_camera:
+ cam_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_CAMERA, video_camera)
+ elif model.ncam > 0:
+ cam_id = 0
+ frame_interval = control_frequency / video_fps # fractional steps per frame
+
+ try:
+ from strands_robots.policies import create_policy as _create_policy
+
+ policy = _create_policy(policy_provider, **policy_kwargs)
+ policy.set_robot_state_keys(robot.joint_names)
+
+ robot.policy_running = True
+ robot.policy_instruction = instruction
+ robot.policy_steps = 0
+ next_frame_step = 0.0
+
+ sim_duration = duration * control_frequency # target number of control steps
+ start_time = time.time()
+ action_sleep = 1.0 / control_frequency
+
+ while robot.policy_steps < sim_duration and robot.policy_running:
+ observation = self._get_sim_observation(robot_name)
+
+ coro_or_result = policy.get_actions(observation, instruction)
+ actions = _resolve_coroutine(coro_or_result)
+
+ for action_dict in actions[:action_horizon]:
+ if not robot.policy_running:
+ break
+
+ if self._world._recording:
+ self._world._trajectory.append(
+ TrajectoryStep(
+ timestamp=time.time(),
+ sim_time=self._world.sim_time,
+ robot_name=robot_name,
+ observation={k: v for k, v in observation.items() if not isinstance(v, np.ndarray)},
+ action=action_dict,
+ instruction=instruction,
+ )
+ )
+ if self._world._dataset_recorder is not None:
+ self._world._dataset_recorder.add_frame(
+ observation=observation,
+ action=action_dict,
+ task=instruction,
+ )
+
+ self._apply_sim_action(robot_name, action_dict)
+ robot.policy_steps += 1
+
+ if writer and robot.policy_steps >= next_frame_step:
+ renderer = self._get_renderer(video_width, video_height)
+ if renderer is not None:
+ if cam_id >= 0:
+ renderer.update_scene(data, camera=cam_id)
+ else:
+ renderer.update_scene(data)
+ writer.append_data(renderer.render().copy())
+ frame_count += 1
+ next_frame_step += frame_interval
+
+ if not fast_mode:
+ time.sleep(action_sleep)
+
+ elapsed = time.time() - start_time
+ robot.policy_running = False
+
+ result_text = (
+ f"✅ Policy complete on '{robot_name}'\n"
+ f"🧠 {policy_provider} | 🎯 {instruction}\n"
+ f"⏱️ {elapsed:.1f}s | 📊 {robot.policy_steps} steps | "
+ f"🕐 sim_t={self._world.sim_time:.3f}s"
+ )
+
+ if writer:
+ writer.close()
+ file_kb = os.path.getsize(record_video) / 1024
+ result_text += (
+ f"\n🎬 Video: {record_video}\n"
+ f"📹 {frame_count} frames, {video_fps}fps, {video_width}x{video_height} | 💾 {file_kb:.0f} KB"
+ )
+
+ return {"status": "success", "content": [{"text": result_text}]}
+
+ except Exception as e:
+ robot.policy_running = False
+ if writer:
+ writer.close()
+ return {"status": "error", "content": [{"text": f"❌ Policy failed: {e}"}]}
+
+ def start_policy(
+ self,
+ robot_name: str,
+ policy_provider: str = "mock",
+ instruction: str = "",
+ duration: float = 10.0,
+ fast_mode: bool = False,
+ **policy_kwargs,
+ ) -> dict[str, Any]:
+ """Start policy execution in background (non-blocking)."""
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+ if robot_name not in self._world.robots:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{robot_name}' not found."}]}
+
+ future = self._executor.submit(
+ self.run_policy,
+ robot_name,
+ policy_provider,
+ instruction,
+ duration,
+ fast_mode=fast_mode,
+ **policy_kwargs,
+ )
+ self._policy_threads[robot_name] = future
+
+ return {
+ "status": "success",
+ "content": [{"text": f"🚀 Policy started on '{robot_name}' (async)"}],
+ }
+
+ def replay_episode(
+ self,
+ repo_id: str,
+ robot_name: str = None,
+ episode: int = 0,
+ root: str = None,
+ speed: float = 1.0,
+ ) -> dict[str, Any]:
+ """Replay actions from a LeRobotDataset episode in simulation."""
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world. Call create_world first."}]}
+
+ if robot_name is None:
+ if not self._world.robots:
+ return {"status": "error", "content": [{"text": "❌ No robots in sim. Add one first."}]}
+ robot_name = next(iter(self._world.robots))
+
+ robot = self._world.robots.get(robot_name)
+ if robot is None:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{robot_name}' not found"}]}
+
+ try:
+ from strands_robots.dataset_recorder import load_lerobot_episode
+
+ ds, episode_start, episode_length = load_lerobot_episode(repo_id, episode, root)
+ except ImportError:
+ return {"status": "error", "content": [{"text": "❌ lerobot not installed"}]}
+ except (ValueError, Exception) as e:
+ return {"status": "error", "content": [{"text": f"❌ {e}"}]}
+
+ mj = _ensure_mujoco()
+ dataset_fps = getattr(ds, "fps", 30)
+ frame_interval = 1.0 / (dataset_fps * speed)
+ model = self._world._model
+ data = self._world._data
+ n_actuators = model.nu
+ frames_applied = 0
+ start_time = time.time()
+
+ for frame_idx in range(episode_length):
+ step_start = time.time()
+ frame = ds[episode_start + frame_idx]
+
+ if "action" in frame:
+ action_vals = frame["action"]
+ if hasattr(action_vals, "numpy"):
+ action_vals = action_vals.numpy()
+ if hasattr(action_vals, "tolist"):
+ action_vals = action_vals.tolist()
+ for i in range(min(len(action_vals), n_actuators)):
+ data.ctrl[i] = float(action_vals[i])
+
+ mj.mj_step(model, data)
+ frames_applied += 1
+
+ elapsed = time.time() - step_start
+ sleep_time = frame_interval - elapsed
+ if sleep_time > 0:
+ time.sleep(sleep_time)
+
+ duration = time.time() - start_time
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"▶️ Replayed episode {episode} from {repo_id} on '{robot_name}'\n"
+ f"Frames: {frames_applied}/{episode_length} | Duration: {duration:.1f}s | Speed: {speed}x"
+ )
+ },
+ {
+ "json": {
+ "episode": episode,
+ "robot_name": robot_name,
+ "frames_applied": frames_applied,
+ "total_frames": episode_length,
+ "duration_s": round(duration, 2),
+ "speed": speed,
+ }
+ },
+ ],
+ }
+
+ def eval_policy(
+ self,
+ robot_name: str = None,
+ policy_provider: str = "mock",
+ instruction: str = "",
+ n_episodes: int = 10,
+ max_steps: int = 300,
+ success_fn: str = None,
+ **policy_kwargs,
+ ) -> dict[str, Any]:
+ """Evaluate a policy over multiple episodes with success metrics."""
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world. Call create_world first."}]}
+
+ if robot_name is None:
+ if not self._world.robots:
+ return {"status": "error", "content": [{"text": "❌ No robots"}]}
+ robot_name = next(iter(self._world.robots))
+
+ robot = self._world.robots.get(robot_name)
+ if robot is None:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{robot_name}' not found"}]}
+
+ from strands_robots.policies import create_policy
+
+ mj = _ensure_mujoco()
+ policy_instance = create_policy(policy_provider, **policy_kwargs)
+ policy_instance.set_robot_state_keys(robot.joint_names)
+
+ model = self._world._model
+ data = self._world._data
+
+ results = []
+ for ep in range(n_episodes):
+ mj.mj_resetData(model, data)
+ mj.mj_forward(model, data)
+
+ total_reward = 0.0
+ success = False
+ steps = 0
+
+ for step in range(max_steps):
+ obs = self._get_sim_observation(robot_name=robot_name)
+ coro_or_result = policy_instance.get_actions(obs, instruction)
+ actions = _resolve_coroutine(coro_or_result)
+
+ if actions:
+ self._apply_sim_action(robot_name, actions[0])
+
+ mj.mj_step(model, data)
+ steps += 1
+
+ if success_fn == "contact":
+ for i in range(data.ncon):
+ if data.contact[i].dist < 0:
+ success = True
+ break
+ if success:
+ break
+
+ results.append({"episode": ep, "steps": steps, "success": success, "reward": total_reward})
+
+ n_success = sum(1 for r in results if r["success"])
+ success_rate = n_success / max(n_episodes, 1)
+ avg_steps = sum(r["steps"] for r in results) / max(n_episodes, 1)
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"📊 Evaluation: {policy_provider} on '{robot_name}'\n"
+ f"Episodes: {n_episodes} | Success: {n_success}/{n_episodes} ({success_rate:.1%})\n"
+ f"Avg steps: {avg_steps:.0f}/{max_steps}"
+ )
+ },
+ {
+ "json": {
+ "success_rate": round(success_rate, 4),
+ "n_episodes": n_episodes,
+ "n_success": n_success,
+ "avg_steps": round(avg_steps, 1),
+ "max_steps": max_steps,
+ "episodes": results,
+ }
+ },
+ ],
+ }
diff --git a/strands_robots/simulation/mujoco/randomization.py b/strands_robots/simulation/mujoco/randomization.py
new file mode 100644
index 0000000..cdb2d3e
--- /dev/null
+++ b/strands_robots/simulation/mujoco/randomization.py
@@ -0,0 +1,74 @@
+"""Domain randomization mixin."""
+
+import logging
+from typing import Any
+
+import numpy as np
+
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+
+logger = logging.getLogger(__name__)
+
+
+class RandomizationMixin:
+ """Domain randomization for Simulation. Expects self._world."""
+
+ def randomize(
+ self,
+ randomize_colors: bool = True,
+ randomize_lighting: bool = True,
+ randomize_physics: bool = False,
+ randomize_positions: bool = False,
+ position_noise: float = 0.02,
+ color_range: tuple[float, float] = (0.1, 1.0),
+ friction_range: tuple[float, float] = (0.5, 1.5),
+ mass_range: tuple[float, float] = (0.5, 2.0),
+ seed: int = None,
+ ) -> dict[str, Any]:
+ """Apply domain randomization to the scene."""
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ rng = np.random.default_rng(seed)
+ mj = _ensure_mujoco()
+ model = self._world._model
+ data = self._world._data
+ changes = []
+
+ if randomize_colors:
+ for i in range(model.ngeom):
+ geom_name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_GEOM, i)
+ if geom_name and geom_name != "ground":
+ model.geom_rgba[i, :3] = rng.uniform(color_range[0], color_range[1], size=3)
+ changes.append(f"🎨 Colors: {model.ngeom} geoms randomized")
+
+ if randomize_lighting:
+ for i in range(model.nlight):
+ model.light_pos[i] += rng.uniform(-0.5, 0.5, size=3)
+ model.light_diffuse[i] = rng.uniform(0.3, 1.0, size=3)
+ changes.append(f"💡 Lighting: {model.nlight} lights randomized")
+
+ if randomize_physics:
+ for i in range(model.ngeom):
+ model.geom_friction[i, 0] *= rng.uniform(*friction_range)
+ for i in range(model.nbody):
+ if model.body_mass[i] > 0:
+ model.body_mass[i] *= rng.uniform(*mass_range)
+ changes.append(f"⚙️ Physics: friction×[{friction_range}], mass×[{mass_range}]")
+
+ if randomize_positions:
+ for obj_name, obj in self._world.objects.items():
+ if not obj.is_static:
+ jnt_name = f"{obj_name}_joint"
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, jnt_name)
+ if jnt_id >= 0:
+ qpos_addr = model.jnt_qposadr[jnt_id]
+ noise = rng.uniform(-position_noise, position_noise, size=3)
+ data.qpos[qpos_addr : qpos_addr + 3] += noise
+ mj.mj_forward(model, data)
+ changes.append(f"📍 Positions: ±{position_noise}m noise on dynamic objects")
+
+ return {
+ "status": "success",
+ "content": [{"text": "🎲 Domain Randomization applied:\n" + "\n".join(changes)}],
+ }
diff --git a/strands_robots/simulation/mujoco/recording.py b/strands_robots/simulation/mujoco/recording.py
new file mode 100644
index 0000000..c2ef006
--- /dev/null
+++ b/strands_robots/simulation/mujoco/recording.py
@@ -0,0 +1,152 @@
+"""Recording mixin — start/stop trajectory recording to LeRobotDataset."""
+
+import logging
+import shutil
+from pathlib import Path
+from typing import Any
+
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+
+logger = logging.getLogger(__name__)
+
+
+class RecordingMixin:
+ """Trajectory recording for Simulation. Expects self._world."""
+
+ def start_recording(
+ self,
+ repo_id: str = "local/sim_recording",
+ task: str = "",
+ fps: int = 30,
+ root: str = None,
+ push_to_hub: bool = False,
+ vcodec: str = "libsvtav1",
+ overwrite: bool = True,
+ ) -> dict[str, Any]:
+ """Start recording to LeRobotDataset format (parquet + video)."""
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "No world."}]}
+
+ try:
+ from strands_robots.dataset_recorder import DatasetRecorder as _DatasetRecorder
+ from strands_robots.dataset_recorder import has_lerobot_dataset as _has_lerobot
+ except ImportError:
+
+ def _has_lerobot():
+ return False
+
+ _DatasetRecorder = None
+
+ if not _has_lerobot() or _DatasetRecorder is None:
+ return {
+ "status": "error",
+ "content": [
+ {
+ "text": "lerobot not installed. Install with: pip install lerobot\nRequired for dataset recording."
+ }
+ ],
+ }
+
+ self._world._recording = True
+ self._world._trajectory = []
+ self._world._push_to_hub = push_to_hub
+
+ try:
+ if overwrite:
+ if root:
+ dataset_dir = Path(root)
+ elif "/" not in repo_id or repo_id.startswith("/") or repo_id.startswith("./"):
+ dataset_dir = Path(repo_id)
+ else:
+ dataset_dir = Path.home() / ".cache" / "huggingface" / "lerobot" / repo_id
+ if dataset_dir.exists() and dataset_dir.is_dir():
+ shutil.rmtree(dataset_dir)
+ logger.info("Removed existing dataset dir: %s", dataset_dir)
+
+ joint_names = []
+ camera_keys = []
+ robot_type = "unknown"
+ for rname, robot in self._world.robots.items():
+ joint_names.extend(robot.joint_names)
+ robot_type = robot.data_config or rname
+
+ mj = _ensure_mujoco()
+ for i in range(self._world._model.ncam):
+ cam_name = mj.mj_id2name(self._world._model, mj.mjtObj.mjOBJ_CAMERA, i)
+ if cam_name:
+ camera_keys.append(cam_name)
+
+ self._world._dataset_recorder = _DatasetRecorder.create(
+ repo_id=repo_id,
+ fps=fps,
+ robot_type=robot_type,
+ joint_names=joint_names,
+ camera_keys=camera_keys,
+ task=task,
+ root=root,
+ vcodec=vcodec,
+ )
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"Recording to LeRobotDataset: {repo_id}\n"
+ f"{len(joint_names)} joints, {len(camera_keys)} cameras @ {fps}fps\n"
+ f"Codec: {vcodec} | Task: {task or '(set per policy)'}\n"
+ f"Run policies to capture frames, then stop_recording to save episode"
+ )
+ }
+ ],
+ }
+ except Exception as e:
+ self._world._recording = False
+ logger.error("Dataset recorder init failed: %s", e)
+ return {"status": "error", "content": [{"text": f"Dataset init failed: {e}"}]}
+
+ def stop_recording(self, output_path: str = None) -> dict[str, Any]:
+ """Stop recording and save episode to LeRobotDataset."""
+ if self._world is None or not self._world._recording:
+ return {"status": "error", "content": [{"text": "Not recording."}]}
+
+ self._world._recording = False
+ recorder = self._world._dataset_recorder
+
+ if recorder is None:
+ return {"status": "error", "content": [{"text": "No dataset recorder active."}]}
+
+ recorder.save_episode()
+ push_result = None
+ if getattr(self._world, "_push_to_hub", False):
+ push_result = recorder.push_to_hub(tags=["strands-robots", "sim"])
+
+ repo_id = recorder.repo_id
+ frame_count = recorder.frame_count
+ episode_count = recorder.episode_count
+ root = recorder.root
+
+ recorder.finalize()
+ self._world._dataset_recorder = None
+ self._world._trajectory = []
+
+ text = (
+ f"Episode saved to LeRobotDataset\n"
+ f"{repo_id} -- {frame_count} frames, {episode_count} episode(s)\n"
+ f"Local: {root}"
+ )
+ if push_result and push_result.get("status") == "success":
+ text += "\nPushed to HuggingFace Hub"
+
+ return {"status": "success", "content": [{"text": text}]}
+
+ def get_recording_status(self) -> dict[str, Any]:
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+
+ recording = self._world._recording
+ steps = len(self._world._trajectory)
+
+ return {
+ "status": "success",
+ "content": [{"text": f"{'🔴 Recording' if recording else '⚪ Not recording'}: {steps} steps captured"}],
+ }
diff --git a/strands_robots/simulation/mujoco/rendering.py b/strands_robots/simulation/mujoco/rendering.py
new file mode 100644
index 0000000..c51fc0c
--- /dev/null
+++ b/strands_robots/simulation/mujoco/rendering.py
@@ -0,0 +1,225 @@
+"""Rendering mixin — render, render_depth, get_contacts, observation helpers."""
+
+import io
+import json
+import logging
+from typing import Any
+
+from strands_robots.simulation.mujoco.backend import _can_render, _ensure_mujoco
+
+logger = logging.getLogger(__name__)
+
+
+class RenderingMixin:
+ """Rendering capabilities for Simulation. Expects self._world, self.default_width, self.default_height."""
+
+ def _get_renderer(self, width: int, height: int):
+ """Get a cached MuJoCo renderer, creating one only if needed.
+
+ Returns None if rendering is unavailable (headless without EGL/OSMesa).
+ Callers must handle None return.
+ """
+ if not _can_render():
+ return None
+ mj = _ensure_mujoco()
+ key = (width, height)
+ if self._renderer_model is not self._world._model:
+ self._renderers.clear()
+ self._renderer_model = self._world._model
+ if key not in self._renderers:
+ self._renderers[key] = mj.Renderer(self._world._model, height=height, width=width)
+ return self._renderers[key]
+
+ def _get_sim_observation(self, robot_name: str, cam_name: str = None) -> dict[str, Any]:
+ """Get observation from sim (same format as real robot)."""
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+ robot = self._world.robots[robot_name]
+
+ obs = {}
+ for jnt_name in robot.joint_names:
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, jnt_name)
+ if jnt_id >= 0:
+ obs[jnt_name] = float(data.qpos[model.jnt_qposadr[jnt_id]])
+
+ cameras_to_render = []
+ if cam_name:
+ cameras_to_render = [cam_name]
+ else:
+ cameras_to_render = [mj.mj_id2name(model, mj.mjtObj.mjOBJ_CAMERA, i) for i in range(model.ncam)]
+ for pycam_name in self._world.cameras:
+ if pycam_name not in cameras_to_render:
+ cameras_to_render.append(pycam_name)
+
+ for cname in cameras_to_render:
+ if not cname:
+ continue
+ cam_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_CAMERA, cname)
+ cam_info = self._world.cameras.get(cname)
+ h = cam_info.height if cam_info else self.default_height
+ w = cam_info.width if cam_info else self.default_width
+ try:
+ renderer = self._get_renderer(w, h)
+ if renderer is None:
+ continue
+ if cam_id >= 0:
+ renderer.update_scene(data, camera=cam_id)
+ else:
+ renderer.update_scene(data)
+ obs[cname] = renderer.render().copy()
+ except (RuntimeError, ValueError) as e:
+ # Individual camera failure shouldn't stop joint state collection.
+ # Common cause: camera ID invalid after scene recompile.
+ logger.debug("Camera render failed for %s: %s", cname, e)
+
+ return obs
+
+ def _apply_sim_action(self, robot_name: str, action_dict: dict[str, Any], n_substeps: int = 1):
+ """Apply action dict to sim (same interface as robot.send_action)."""
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ for key, value in action_dict.items():
+ act_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_ACTUATOR, key)
+ if act_id >= 0:
+ data.ctrl[act_id] = float(value)
+ else:
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, key)
+ if jnt_id >= 0 and jnt_id < model.nu:
+ data.ctrl[jnt_id] = float(value)
+
+ for _ in range(max(1, n_substeps)):
+ mj.mj_step(model, data)
+
+ self._world.sim_time = data.time
+ self._world.step_count += n_substeps
+
+ if hasattr(self, "_viewer_handle") and self._viewer_handle is not None:
+ self._viewer_handle.sync()
+
+ def render(self, camera_name: str = "default", width: int = None, height: int = None) -> dict[str, Any]:
+ """Render a camera view as base64 PNG image."""
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ w = width or self.default_width
+ h = height or self.default_height
+
+ try:
+ renderer = self._get_renderer(w, h)
+ if renderer is None:
+ return {
+ "status": "error",
+ "content": [
+ {
+ "text": (
+ "❌ Rendering unavailable (no OpenGL context). "
+ "Install EGL or OSMesa for offscreen rendering: "
+ "apt-get install libosmesa6-dev"
+ )
+ }
+ ],
+ }
+ cam_id = mj.mj_name2id(self._world._model, mj.mjtObj.mjOBJ_CAMERA, camera_name)
+ if cam_id >= 0:
+ renderer.update_scene(self._world._data, camera=cam_id)
+ else:
+ renderer.update_scene(self._world._data)
+
+ img = renderer.render().copy()
+
+ from PIL import Image
+
+ pil_img = Image.fromarray(img)
+ buffer = io.BytesIO()
+ pil_img.save(buffer, format="PNG")
+ png_bytes = buffer.getvalue()
+
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"📸 {w}x{h} from '{camera_name}' at t={self._world.sim_time:.3f}s"},
+ {"image": {"format": "png", "source": {"bytes": png_bytes}}},
+ ],
+ }
+ except Exception as e:
+ return {"status": "error", "content": [{"text": f"❌ Render failed: {e}"}]}
+
+ def render_depth(self, camera_name: str = "default", width: int = None, height: int = None) -> dict[str, Any]:
+ """Render depth map from a camera."""
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ w = width or self.default_width
+ h = height or self.default_height
+
+ try:
+ cam_id = -1
+ if camera_name and camera_name != "default":
+ cam_id = mj.mj_name2id(self._world._model, mj.mjtObj.mjOBJ_CAMERA, camera_name)
+
+ renderer = self._get_renderer(w, h)
+ if renderer is None:
+ return {
+ "status": "error",
+ "content": [
+ {
+ "text": (
+ "❌ Depth rendering unavailable (no OpenGL context). "
+ "Install EGL or OSMesa for offscreen rendering."
+ )
+ }
+ ],
+ }
+ if cam_id >= 0:
+ renderer.update_scene(self._world._data, camera=cam_id)
+ else:
+ renderer.update_scene(self._world._data)
+ renderer.enable_depth_rendering()
+ depth = renderer.render()
+ renderer.disable_depth_rendering()
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"📸 Depth {w}x{h} from '{camera_name}'\n"
+ f"Min: {float(depth.min()):.3f}m, Max: {float(depth.max()):.3f}m"
+ )
+ },
+ {
+ "text": json.dumps(
+ {"depth_min": float(depth.min()), "depth_max": float(depth.max())}, default=str
+ )
+ },
+ ],
+ }
+ except Exception as e:
+ return {"status": "error", "content": [{"text": f"❌ Depth render failed: {e}"}]}
+
+ def get_contacts(self) -> dict[str, Any]:
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ contacts = []
+ for i in range(data.ncon):
+ c = data.contact[i]
+ g1 = mj.mj_id2name(model, mj.mjtObj.mjOBJ_GEOM, c.geom1) or f"geom_{c.geom1}"
+ g2 = mj.mj_id2name(model, mj.mjtObj.mjOBJ_GEOM, c.geom2) or f"geom_{c.geom2}"
+ contacts.append({"geom1": g1, "geom2": g2, "dist": float(c.dist), "pos": c.pos.tolist()})
+
+ text = f"💥 {len(contacts)} contacts" if contacts else "No contacts."
+ if contacts:
+ for c in contacts[:10]:
+ text += f"\n • {c['geom1']} ↔ {c['geom2']} (d={c['dist']:.4f})"
+
+ return {
+ "status": "success",
+ "content": [{"text": text}, {"text": json.dumps({"contacts": contacts}, default=str)}],
+ }
diff --git a/strands_robots/simulation/mujoco/scene_ops.py b/strands_robots/simulation/mujoco/scene_ops.py
new file mode 100644
index 0000000..ba83696
--- /dev/null
+++ b/strands_robots/simulation/mujoco/scene_ops.py
@@ -0,0 +1,211 @@
+"""XML round-trip injection/ejection for scene modification.
+
+Shared helper `_reload_scene_from_xml` handles the common pattern:
+save XML → patch paths → modify → reload → copy state → re-discover joints.
+"""
+
+import logging
+import os
+import re
+import shutil
+import tempfile
+import xml.etree.ElementTree as ET
+
+from strands_robots.simulation.models import SimCamera, SimObject, SimWorld
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+from strands_robots.simulation.mujoco.mjcf_builder import MJCFBuilder
+
+logger = logging.getLogger(__name__)
+
+
+def _patch_xml_paths(xml_content: str, robot_base_dir: str) -> str:
+ """Patch meshdir/texturedir in XML to absolute paths for tmpdir loading."""
+ meshdir_match = re.search(r'meshdir="([^"]*)"', xml_content)
+ existing_meshdir = meshdir_match.group(1) if meshdir_match else ""
+ abs_meshdir = os.path.normpath(os.path.join(robot_base_dir, existing_meshdir))
+
+ texdir_match = re.search(r'texturedir="([^"]*)"', xml_content)
+ existing_texdir = texdir_match.group(1) if texdir_match else ""
+ abs_texdir = os.path.normpath(os.path.join(robot_base_dir, existing_texdir))
+
+ if meshdir_match:
+ xml_content = re.sub(r'meshdir="[^"]*"', f'meshdir="{abs_meshdir}"', xml_content)
+ elif "
bool:
+ """Reload MuJoCo model from modified XML, preserving state.
+
+ Copies qpos, qvel, ctrl from old model and re-discovers robot joint/actuator IDs.
+ """
+ mj = _ensure_mujoco()
+ new_model = mj.MjModel.from_xml_path(str(scene_path))
+ new_data = mj.MjData(new_model)
+
+ # Copy state from old model
+ old_nq = min(world._data.qpos.shape[0], new_data.qpos.shape[0])
+ old_nv = min(world._data.qvel.shape[0], new_data.qvel.shape[0])
+ new_data.qpos[:old_nq] = world._data.qpos[:old_nq]
+ new_data.qvel[:old_nv] = world._data.qvel[:old_nv]
+ old_nu = min(world._data.ctrl.shape[0], new_data.ctrl.shape[0])
+ new_data.ctrl[:old_nu] = world._data.ctrl[:old_nu]
+
+ mj.mj_forward(new_model, new_data)
+
+ world._model = new_model
+ world._data = new_data
+
+ # Re-discover robot joints/actuators (IDs may shift)
+ for robot in world.robots.values():
+ robot.joint_ids = []
+ robot.actuator_ids = []
+ for jnt_name in robot.joint_names:
+ jid = mj.mj_name2id(new_model, mj.mjtObj.mjOBJ_JOINT, jnt_name)
+ if jid >= 0:
+ robot.joint_ids.append(jid)
+ for i in range(new_model.nu):
+ jnt_id = new_model.actuator_trnid[i, 0]
+ if jnt_id in robot.joint_ids:
+ robot.actuator_ids.append(i)
+ if not robot.actuator_ids:
+ for i in range(new_model.nu):
+ robot.actuator_ids.append(i)
+
+ return True
+
+
+def _get_robot_base_dir(world: SimWorld) -> str | None:
+ """Get the directory of the original robot model file."""
+ if world._robot_base_xml:
+ return os.path.dirname(os.path.abspath(world._robot_base_xml))
+ return None
+
+
+def _save_and_patch_xml(world: SimWorld, tmpdir: str, filename: str) -> str:
+ """Save current model to XML in tmpdir and patch asset paths."""
+ mj = _ensure_mujoco()
+ scene_path = os.path.join(tmpdir, filename)
+ mj.mj_saveLastXML(scene_path, world._model)
+
+ robot_base_dir = _get_robot_base_dir(world)
+ if robot_base_dir and os.path.isdir(robot_base_dir):
+ with open(scene_path) as f:
+ xml_content = f.read()
+ xml_content = _patch_xml_paths(xml_content, robot_base_dir)
+ with open(scene_path, "w") as f:
+ f.write(xml_content)
+
+ return scene_path
+
+
+def inject_object_into_scene(world: SimWorld, obj: SimObject) -> bool:
+ """Inject object into a running simulation via XML round-trip."""
+ _ensure_mujoco()
+ if world._model is None:
+ return False
+
+ tmpdir = tempfile.mkdtemp(prefix="strands_sim_")
+ try:
+ scene_path = _save_and_patch_xml(world, tmpdir, "scene_with_objects.xml")
+
+ with open(scene_path) as f:
+ xml_content = f.read()
+
+ obj_xml = MJCFBuilder._object_xml(obj, indent=4)
+ xml_content = xml_content.replace("", f"{obj_xml}\n")
+
+ # Remove keyframes — adding a freejoint changes qpos size
+ xml_content = re.sub(r".*?", "", xml_content, flags=re.DOTALL)
+
+ with open(scene_path, "w") as f:
+ f.write(xml_content)
+
+ return _reload_scene_from_xml(world, scene_path)
+ except (ValueError, RuntimeError, OSError) as e:
+ logger.error("Object injection reload failed: %s", e)
+ return False
+ finally:
+ shutil.rmtree(tmpdir, ignore_errors=True)
+
+
+def eject_body_from_scene(world: SimWorld, body_name: str) -> bool:
+ """Remove a named body from the scene via XML round-trip."""
+ mj = _ensure_mujoco()
+
+ tmpdir = tempfile.mkdtemp(prefix="strands_eject_")
+ try:
+ scene_path = os.path.join(tmpdir, "scene_ejected.xml")
+ mj.mj_saveLastXML(scene_path, world._model)
+
+ tree = ET.parse(scene_path)
+ root = tree.getroot()
+
+ # Patch paths
+ robot_base_dir = _get_robot_base_dir(world)
+ if robot_base_dir:
+ compiler = root.find("compiler")
+ if compiler is not None:
+ existing_meshdir = compiler.get("meshdir", "")
+ compiler.set("meshdir", os.path.normpath(os.path.join(robot_base_dir, existing_meshdir)))
+ existing_texdir = compiler.get("texturedir", "")
+ compiler.set("texturedir", os.path.normpath(os.path.join(robot_base_dir, existing_texdir)))
+
+ # Remove target body
+ removed = False
+ for parent in root.iter():
+ for child in list(parent):
+ if child.tag == "body" and child.get("name") == body_name:
+ parent.remove(child)
+ removed = True
+
+ if not removed:
+ logger.warning(f"Body '{body_name}' not found in MJCF XML — skipping ejection.")
+
+ # Remove keyframes
+ for keyframe_elem in root.findall("keyframe"):
+ root.remove(keyframe_elem)
+
+ tree.write(scene_path, xml_declaration=True)
+
+ return _reload_scene_from_xml(world, scene_path)
+ except (ValueError, RuntimeError, OSError) as e:
+ logger.error("Body ejection failed for '%s': %s", body_name, e)
+ return False
+ finally:
+ shutil.rmtree(tmpdir, ignore_errors=True)
+
+
+def inject_camera_into_scene(world: SimWorld, cam: SimCamera) -> bool:
+ """Inject a camera into a running simulation via XML round-trip."""
+ _ensure_mujoco()
+ if world._model is None:
+ return False
+
+ tmpdir = tempfile.mkdtemp(prefix="strands_cam_")
+ try:
+ scene_path = _save_and_patch_xml(world, tmpdir, "scene_with_cameras.xml")
+
+ with open(scene_path) as f:
+ xml_content = f.read()
+
+ px, py, pz = cam.position
+ cam_xml = f' '
+ xml_content = xml_content.replace("", f"{cam_xml}\n")
+
+ with open(scene_path, "w") as f:
+ f.write(xml_content)
+
+ return _reload_scene_from_xml(world, scene_path)
+ except (ValueError, RuntimeError, OSError) as e:
+ logger.error("Camera injection reload failed: %s", e)
+ return False
+ finally:
+ shutil.rmtree(tmpdir, ignore_errors=True)
diff --git a/strands_robots/simulation/mujoco/simulation.py b/strands_robots/simulation/mujoco/simulation.py
new file mode 100644
index 0000000..70b5404
--- /dev/null
+++ b/strands_robots/simulation/mujoco/simulation.py
@@ -0,0 +1,949 @@
+"""MuJoCo Simulation — AgentTool orchestrator composing physics/rendering/policy mixins."""
+
+import json
+import logging
+import os
+import re
+import threading
+from collections.abc import AsyncGenerator
+from concurrent.futures import Future, ThreadPoolExecutor
+from pathlib import Path
+from typing import Any
+
+from strands.tools.tools import AgentTool
+from strands.types._events import ToolResultEvent
+from strands.types.tools import ToolSpec, ToolUse
+
+from strands_robots.simulation.model_registry import (
+ list_available_models,
+ register_urdf,
+ resolve_model,
+)
+from strands_robots.simulation.models import SimCamera, SimObject, SimRobot, SimStatus, SimWorld
+from strands_robots.simulation.mujoco.backend import _ensure_mujoco
+from strands_robots.simulation.mujoco.mjcf_builder import MJCFBuilder
+from strands_robots.simulation.mujoco.physics import PhysicsMixin
+from strands_robots.simulation.mujoco.policy_runner import PolicyRunnerMixin
+from strands_robots.simulation.mujoco.randomization import RandomizationMixin
+from strands_robots.simulation.mujoco.recording import RecordingMixin
+from strands_robots.simulation.mujoco.rendering import RenderingMixin
+from strands_robots.simulation.mujoco.scene_ops import (
+ eject_body_from_scene,
+ inject_camera_into_scene,
+ inject_object_into_scene,
+)
+
+logger = logging.getLogger(__name__)
+
+_TOOL_SPEC_PATH = Path(__file__).parent / "tool_spec.json"
+
+
+class Simulation(
+ PhysicsMixin,
+ PolicyRunnerMixin,
+ RenderingMixin,
+ RecordingMixin,
+ RandomizationMixin,
+ AgentTool,
+):
+ """Programmatic simulation environment as a Strands AgentTool.
+
+ Gives AI agents the ability to create, modify, and control MuJoCo
+ simulation environments through natural language → tool actions.
+ """
+
+ def __init__(
+ self,
+ tool_name: str = "sim",
+ default_timestep: float = 0.002,
+ default_width: int = 640,
+ default_height: int = 480,
+ mesh: bool = True,
+ peer_id: str = None,
+ **kwargs,
+ ):
+ super().__init__()
+ self.tool_name_str = tool_name
+ self.default_timestep = default_timestep
+ self.default_width = default_width
+ self.default_height = default_height
+
+ self._world: SimWorld | None = None
+ self._executor = ThreadPoolExecutor(max_workers=4, thread_name_prefix=f"{tool_name}_sim")
+ self._policy_threads: dict[str, Future] = {}
+ self._shutdown_event = threading.Event()
+ self._lock = threading.Lock()
+
+ self._viewer_handle = None
+ self._viewer_thread = None
+
+ self._renderers: dict[tuple, Any] = {}
+ self._renderer_model = None
+
+ logger.info("🎮 Simulation tool '%s' initialized", tool_name)
+
+ try:
+ from strands_robots.zenoh_mesh import init_mesh
+
+ self.mesh = init_mesh(self, peer_id=peer_id, peer_type="sim", mesh=mesh)
+ except Exception as e:
+ logger.debug("Mesh init skipped: %s", e)
+ self.mesh = None
+
+ # --- Public Properties ---
+
+ @property
+ def mj_model(self):
+ """Direct access to the MuJoCo model (mujoco.MjModel)."""
+ return self._world._model if self._world else None
+
+ @property
+ def mj_data(self):
+ """Direct access to the MuJoCo data (mujoco.MjData)."""
+ return self._world._data if self._world else None
+
+ # --- Robot-compatible interface ---
+
+ def get_observation(self, robot_name: str = None, camera_name: str = None) -> dict[str, Any]:
+ """Get observation from simulation (Robot ABC compatible)."""
+ if self._world is None or self._world._model is None:
+ return {}
+ if robot_name is None:
+ if not self._world.robots:
+ return {}
+ robot_name = next(iter(self._world.robots))
+ if robot_name not in self._world.robots:
+ return {}
+ return self._get_sim_observation(robot_name, cam_name=camera_name)
+
+ def send_action(self, action: dict[str, Any], robot_name: str = None, n_substeps: int = 1) -> None:
+ """Apply action to simulation (Robot ABC compatible)."""
+ if self._world is None or self._world._model is None:
+ return
+ if robot_name is None:
+ if not self._world.robots:
+ return
+ robot_name = next(iter(self._world.robots))
+ if robot_name not in self._world.robots:
+ return
+ self._apply_sim_action(robot_name, action, n_substeps=n_substeps)
+
+ # --- World Management ---
+
+ def _cheap_robot_count(self) -> int:
+ try:
+ from strands_robots.registry import list_robots as _registry_list_robots
+
+ return len(_registry_list_robots(mode="sim"))
+ except ImportError:
+ return 0
+
+ def create_world(
+ self, timestep: float = None, gravity: list[float] = None, ground_plane: bool = True
+ ) -> dict[str, Any]:
+ """Create a new simulation world."""
+ _ensure_mujoco()
+
+ if self._world is not None and self._world._model is not None:
+ return {
+ "status": "error",
+ "content": [{"text": "❌ World already exists. Use action='destroy' first, or action='reset'."}],
+ }
+
+ if gravity is None:
+ _gravity = [0.0, 0.0, -9.81]
+ elif isinstance(gravity, (int, float)):
+ _gravity = [0.0, 0.0, float(gravity)]
+ else:
+ _gravity = list(gravity)
+
+ self._world = SimWorld(
+ timestep=timestep or self.default_timestep,
+ gravity=_gravity,
+ ground_plane=ground_plane,
+ )
+
+ self._world.cameras["default"] = SimCamera(
+ name="default",
+ position=[1.5, 1.5, 1.2],
+ target=[0.0, 0.0, 0.3],
+ width=self.default_width,
+ height=self.default_height,
+ )
+
+ self._compile_world()
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ "🌍 Simulation world created\n"
+ f"⚙️ Timestep: {self._world.timestep}s ({1 / self._world.timestep:.0f}Hz physics)\n"
+ f"🌐 Gravity: {self._world.gravity}\n"
+ f"📷 Default camera ready\n"
+ f"🤖 Robot models: {self._cheap_robot_count()} available\n"
+ "💡 Add robots: action='add_robot' (urdf_path or data_config)\n"
+ "💡 Add objects: action='add_object'\n"
+ "💡 List URDFs: action='list_urdfs'"
+ )
+ }
+ ],
+ }
+
+ def load_scene(self, scene_path: str) -> dict[str, Any]:
+ """Load a complete scene from MJCF XML or URDF file."""
+ mj = _ensure_mujoco()
+
+ if not os.path.exists(scene_path):
+ return {"status": "error", "content": [{"text": f"❌ Scene file not found: {scene_path}"}]}
+
+ try:
+ self._world = SimWorld()
+ self._world._model = mj.MjModel.from_xml_path(str(scene_path))
+ self._world._data = mj.MjData(self._world._model)
+ self._world.status = SimStatus.IDLE
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"🌍 Scene loaded from {os.path.basename(scene_path)}\n"
+ f"🦴 Bodies: {self._world._model.nbody}, 🔩 Joints: {self._world._model.njnt}, ⚡ Actuators: {self._world._model.nu}\n"
+ "💡 Use action='get_state' to inspect, action='step' to simulate"
+ )
+ }
+ ],
+ }
+ except Exception as e:
+ logger.error("Failed to load scene: %s", e)
+ return {"status": "error", "content": [{"text": f"❌ Failed to load scene: {e}"}]}
+
+ def _compile_world(self):
+ mj = _ensure_mujoco()
+ xml = MJCFBuilder.build_objects_only(self._world)
+ self._world._xml = xml
+ self._world._model = mj.MjModel.from_xml_string(xml)
+ self._world._data = mj.MjData(self._world._model)
+ self._world.status = SimStatus.IDLE
+
+ def _recompile_world(self) -> dict[str, Any]:
+ try:
+ self._compile_world()
+ return {"status": "success"}
+ except Exception as e:
+ return {"status": "error", "content": [{"text": f"❌ Recompile failed: {e}"}]}
+
+ # --- Robot Management ---
+
+ @staticmethod
+ def _ensure_meshes(model_path: str, robot_name: str):
+ """Check if mesh files referenced by a model XML exist; auto-download if missing."""
+ model_dir = os.path.dirname(os.path.abspath(model_path))
+
+ files_to_check = [model_path]
+ try:
+ with open(model_path) as _f:
+ top_content = _f.read()
+ for inc in re.findall(r' dict[str, Any]:
+ """Add a robot to the simulation."""
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world. Use action='create_world' first."}]}
+ if name in self._world.robots:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{name}' already exists."}]}
+
+ resolved_path = urdf_path
+ if not resolved_path and data_config:
+ resolved_path = resolve_model(data_config)
+ if not resolved_path:
+ return {
+ "status": "error",
+ "content": [
+ {
+ "text": f"❌ No model found for '{data_config}'.\n💡 Use action='list_urdfs' to see available robots"
+ }
+ ],
+ }
+ elif not resolved_path and name:
+ resolved_path = resolve_model(name)
+
+ if not resolved_path:
+ return {"status": "error", "content": [{"text": "❌ Either urdf_path or data_config is required."}]}
+ if not os.path.exists(resolved_path):
+ return {"status": "error", "content": [{"text": f"❌ File not found: {resolved_path}"}]}
+
+ mj = _ensure_mujoco()
+
+ robot = SimRobot(
+ name=name,
+ urdf_path=resolved_path,
+ position=position or [0.0, 0.0, 0.0],
+ orientation=orientation or [1.0, 0.0, 0.0, 0.0],
+ data_config=data_config,
+ namespace=f"{name}/",
+ )
+
+ try:
+ self._ensure_meshes(resolved_path, data_config or name)
+
+ model = mj.MjModel.from_xml_path(str(resolved_path))
+ data = mj.MjData(model)
+
+ joint_names = []
+ for i in range(model.njnt):
+ jnt_name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_JOINT, i)
+ if jnt_name:
+ joint_names.append(jnt_name)
+ robot.joint_ids.append(i)
+ robot.joint_names = joint_names
+
+ for i in range(model.nu):
+ act_name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_ACTUATOR, i)
+ if act_name:
+ jnt_id = model.actuator_trnid[i, 0]
+ if jnt_id in robot.joint_ids:
+ robot.actuator_ids.append(i)
+ else:
+ robot.actuator_ids.append(i)
+ if not robot.actuator_ids:
+ for i in range(model.nu):
+ robot.actuator_ids.append(i)
+
+ for i in range(model.ncam):
+ cam_name = mj.mj_id2name(model, mj.mjtObj.mjOBJ_CAMERA, i)
+ if cam_name and cam_name not in self._world.cameras:
+ self._world.cameras[cam_name] = SimCamera(
+ name=cam_name,
+ camera_id=i,
+ width=self.default_width,
+ height=self.default_height,
+ )
+
+ self._world._model = model
+ self._world._data = data
+ self._world._robot_base_xml = resolved_path
+ self._world.robots[name] = robot
+
+ for _ in range(100):
+ mj.mj_step(model, data)
+
+ source = f"data_config='{data_config}'" if data_config else os.path.basename(resolved_path)
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"🤖 Robot '{name}' added to simulation\n"
+ f"📁 Source: {source} → {os.path.basename(resolved_path)}\n"
+ f"📍 Position: {robot.position}\n"
+ f"🔩 Joints: {len(robot.joint_names)} ({', '.join(robot.joint_names[:8])}{'...' if len(robot.joint_names) > 8 else ''})\n"
+ f"⚡ Actuators: {len(robot.actuator_ids)}\n"
+ f"📷 Cameras: {list(self._world.cameras.keys())}\n"
+ f"💡 Run policy: action='run_policy', robot_name='{name}'"
+ )
+ }
+ ],
+ }
+ except Exception as e:
+ logger.error("Failed to add robot '%s': %s", name, e)
+ return {"status": "error", "content": [{"text": f"❌ Failed to load: {e}"}]}
+
+ def remove_robot(self, name: str) -> dict[str, Any]:
+ if self._world is None or name not in self._world.robots:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{name}' not found."}]}
+ if name in self._policy_threads:
+ self._world.robots[name].policy_running = False
+ try:
+ self._policy_threads[name].result(timeout=5.0)
+ except Exception:
+ pass
+ del self._policy_threads[name]
+ del self._world.robots[name]
+ return {"status": "success", "content": [{"text": f"🗑️ Robot '{name}' removed."}]}
+
+ def list_robots(self) -> dict[str, Any]:
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ if not self._world.robots:
+ return {"status": "success", "content": [{"text": "No robots. Use action='add_robot'."}]}
+
+ lines = ["🤖 Robots in simulation:\n"]
+ for name, robot in self._world.robots.items():
+ status = "🟢 running" if robot.policy_running else "⚪ idle"
+ lines.append(
+ f" • {name} ({os.path.basename(robot.urdf_path)})\n"
+ f" Position: {robot.position}, Joints: {len(robot.joint_names)}, "
+ f"Config: {robot.data_config or 'direct'}, Status: {status}"
+ )
+ return {"status": "success", "content": [{"text": "\n".join(lines)}]}
+
+ def get_robot_state(self, robot_name: str) -> dict[str, Any]:
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation running."}]}
+ if robot_name not in self._world.robots:
+ return {"status": "error", "content": [{"text": f"❌ Robot '{robot_name}' not found."}]}
+
+ mj = _ensure_mujoco()
+ robot = self._world.robots[robot_name]
+ model, data = self._world._model, self._world._data
+
+ state = {}
+ for jnt_name in robot.joint_names:
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, jnt_name)
+ if jnt_id >= 0:
+ state[jnt_name] = {
+ "position": float(data.qpos[model.jnt_qposadr[jnt_id]]),
+ "velocity": float(data.qvel[model.jnt_dofadr[jnt_id]]),
+ }
+
+ text = f"🤖 '{robot_name}' state (t={self._world.sim_time:.3f}s):\n"
+ for jnt, vals in state.items():
+ text += f" {jnt}: pos={vals['position']:.4f}, vel={vals['velocity']:.4f}\n"
+
+ return {"status": "success", "content": [{"text": text}, {"text": json.dumps({"state": state}, default=str)}]}
+
+ # --- Object Management ---
+
+ def add_object(
+ self,
+ name: str,
+ shape: str = "box",
+ position: list[float] = None,
+ orientation: list[float] = None,
+ size: list[float] = None,
+ color: list[float] = None,
+ mass: float = 0.1,
+ is_static: bool = False,
+ mesh_path: str = None,
+ ) -> dict[str, Any]:
+ """Add an object to the simulation."""
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ if name in self._world.objects:
+ return {"status": "error", "content": [{"text": f"❌ Object '{name}' exists."}]}
+
+ obj = SimObject(
+ name=name,
+ shape=shape,
+ position=position or [0.0, 0.0, 0.0],
+ orientation=orientation or [1.0, 0.0, 0.0, 0.0],
+ size=size or [0.05, 0.05, 0.05],
+ color=color or [0.5, 0.5, 0.5, 1.0],
+ mass=mass,
+ mesh_path=mesh_path,
+ is_static=is_static,
+ )
+ self._world.objects[name] = obj
+
+ if self._world.robots:
+ try:
+ result = inject_object_into_scene(self._world, obj)
+ if result:
+ return {
+ "status": "success",
+ "content": [{"text": f"📦 '{name}' spawned: {shape} at {obj.position}"}],
+ }
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": (
+ f"📦 '{name}' registered: {shape} at {obj.position}\n"
+ "⚠️ Robot scene loaded — object is tracked but not physically spawned."
+ )
+ }
+ ],
+ }
+ except (ValueError, RuntimeError) as e:
+ raise RuntimeError(
+ f"Object injection into live scene failed for '{name}': {e}. "
+ f"Check that the MJCF XML is valid and compatible with the current scene."
+ ) from e
+
+ result = self._recompile_world()
+ if result["status"] == "error":
+ del self._world.objects[name]
+ return result
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "text": f"📦 '{name}' added: {shape} at {obj.position}, size={obj.size}, {'static' if is_static else f'{mass}kg'}"
+ }
+ ],
+ }
+
+ def remove_object(self, name: str) -> dict[str, Any]:
+ if self._world is None or name not in self._world.objects:
+ return {"status": "error", "content": [{"text": f"❌ Object '{name}' not found."}]}
+ del self._world.objects[name]
+ if self._world.robots:
+ eject_body_from_scene(self._world, name)
+ else:
+ self._recompile_world()
+ return {"status": "success", "content": [{"text": f"🗑️ '{name}' removed."}]}
+
+ def move_object(self, name: str, position: list[float] = None, orientation: list[float] = None) -> dict[str, Any]:
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+ if name not in self._world.objects:
+ return {"status": "error", "content": [{"text": f"❌ '{name}' not found."}]}
+
+ mj = _ensure_mujoco()
+ model, data = self._world._model, self._world._data
+
+ jnt_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, f"{name}_joint")
+ if jnt_id >= 0:
+ qpos_addr = model.jnt_qposadr[jnt_id]
+ if position:
+ data.qpos[qpos_addr : qpos_addr + 3] = position
+ self._world.objects[name].position = position
+ if orientation:
+ data.qpos[qpos_addr + 3 : qpos_addr + 7] = orientation
+ self._world.objects[name].orientation = orientation
+ mj.mj_forward(model, data)
+
+ return {"status": "success", "content": [{"text": f"📍 '{name}' moved to {position or 'same'}"}]}
+
+ def list_objects(self) -> dict[str, Any]:
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ if not self._world.objects:
+ return {"status": "success", "content": [{"text": "No objects."}]}
+
+ lines = ["📦 Objects:\n"]
+ for name, obj in self._world.objects.items():
+ lines.append(f" • {name}: {obj.shape} at {obj.position}, {'static' if obj.is_static else f'{obj.mass}kg'}")
+ return {"status": "success", "content": [{"text": "\n".join(lines)}]}
+
+ # --- Camera Management ---
+
+ def add_camera(
+ self,
+ name: str,
+ position: list[float] = None,
+ target: list[float] = None,
+ fov: float = 60.0,
+ width: int = 640,
+ height: int = 480,
+ ) -> dict[str, Any]:
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+
+ cam = SimCamera(
+ name=name,
+ position=position or [1.0, 1.0, 1.0],
+ target=target or [0.0, 0.0, 0.0],
+ fov=fov,
+ width=width,
+ height=height,
+ )
+ self._world.cameras[name] = cam
+
+ if self._world.robots and self._world._model is not None:
+ try:
+ inject_camera_into_scene(self._world, cam)
+ except (ValueError, RuntimeError) as e:
+ raise RuntimeError(
+ f"Camera injection into live scene failed for '{name}': {e}. "
+ f"Check that camera parameters are valid."
+ ) from e
+ else:
+ self._recompile_world()
+
+ return {"status": "success", "content": [{"text": f"📷 Camera '{name}' added at {cam.position}"}]}
+
+ def remove_camera(self, name: str) -> dict[str, Any]:
+ if self._world is None or name not in self._world.cameras:
+ return {"status": "error", "content": [{"text": f"❌ Camera '{name}' not found."}]}
+ del self._world.cameras[name]
+ return {"status": "success", "content": [{"text": f"🗑️ Camera '{name}' removed."}]}
+
+ # --- Simulation Control ---
+
+ def step(self, n_steps: int = 1) -> dict[str, Any]:
+ if self._world is None or self._world._data is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+ mj = _ensure_mujoco()
+ for _ in range(n_steps):
+ mj.mj_step(self._world._model, self._world._data)
+ self._world.sim_time = self._world._data.time
+ self._world.step_count += n_steps
+ return {
+ "status": "success",
+ "content": [
+ {"text": f"⏩ +{n_steps} steps | t={self._world.sim_time:.4f}s | total={self._world.step_count}"}
+ ],
+ }
+
+ def reset(self) -> dict[str, Any]:
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ mj = _ensure_mujoco()
+ mj.mj_resetData(self._world._model, self._world._data)
+ self._world.sim_time = 0.0
+ self._world.step_count = 0
+ for r in self._world.robots.values():
+ r.policy_running = False
+ r.policy_steps = 0
+ return {"status": "success", "content": [{"text": "🔄 Reset to initial state."}]}
+
+ def get_state(self) -> dict[str, Any]:
+ if self._world is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ lines = [
+ "🌍 Simulation State",
+ f"🕐 t={self._world.sim_time:.4f}s (step {self._world.step_count})",
+ f"⚙️ dt={self._world.timestep}s | 🌐 g={self._world.gravity}",
+ f"🤖 Robots: {len(self._world.robots)} | 📦 Objects: {len(self._world.objects)} | 📷 Cameras: {len(self._world.cameras)}",
+ ]
+ if self._world._model:
+ lines.append(
+ f"🦴 Bodies: {self._world._model.nbody} | 🔩 Joints: {self._world._model.njnt} | ⚡ Actuators: {self._world._model.nu}"
+ )
+ if self._world._recording:
+ lines.append(f"🔴 Recording: {len(self._world._trajectory)} steps")
+ return {"status": "success", "content": [{"text": "\n".join(lines)}]}
+
+ def destroy(self) -> dict[str, Any]:
+ if self._world is None:
+ return {"status": "success", "content": [{"text": "No world to destroy."}]}
+ for r in self._world.robots.values():
+ r.policy_running = False
+ self._close_viewer()
+ self._world = None
+ return {"status": "success", "content": [{"text": "🗑️ World destroyed."}]}
+
+ def set_gravity(self, gravity) -> dict[str, Any]:
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ if isinstance(gravity, (int, float)):
+ gravity = [0.0, 0.0, float(gravity)]
+ self._world._model.opt.gravity[:] = gravity
+ self._world.gravity = gravity
+ return {"status": "success", "content": [{"text": f"🌐 Gravity: {gravity}"}]}
+
+ def set_timestep(self, timestep: float) -> dict[str, Any]:
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No world."}]}
+ self._world._model.opt.timestep = timestep
+ self._world.timestep = timestep
+ return {"status": "success", "content": [{"text": f"⏱️ Timestep: {timestep}s ({1 / timestep:.0f}Hz)"}]}
+
+ # --- Viewer ---
+
+ def open_viewer(self) -> dict[str, Any]:
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation to view."}]}
+ from strands_robots.simulation.mujoco.backend import _mujoco_viewer
+
+ if _mujoco_viewer is None:
+ return {"status": "error", "content": [{"text": "❌ mujoco.viewer not available."}]}
+ if self._viewer_handle is not None:
+ return {"status": "success", "content": [{"text": "👁️ Viewer already open."}]}
+ try:
+ self._viewer_handle = _mujoco_viewer.launch_passive(self._world._model, self._world._data)
+ return {"status": "success", "content": [{"text": "👁️ Interactive viewer opened."}]}
+ except Exception as e:
+ return {"status": "error", "content": [{"text": f"❌ Viewer failed: {e}"}]}
+
+ def _close_viewer(self):
+ if self._viewer_handle is not None:
+ try:
+ self._viewer_handle.close()
+ except Exception:
+ pass
+ self._viewer_handle = None
+
+ def close_viewer(self) -> dict[str, Any]:
+ self._close_viewer()
+ return {"status": "success", "content": [{"text": "👁️ Viewer closed."}]}
+
+ # --- URDF Registry ---
+
+ def list_urdfs_action(self) -> dict[str, Any]:
+ return {"status": "success", "content": [{"text": list_available_models()}]}
+
+ def register_urdf_action(self, data_config: str, urdf_path: str) -> dict[str, Any]:
+ register_urdf(data_config, urdf_path)
+ resolved = resolve_model(data_config)
+ return {
+ "status": "success",
+ "content": [{"text": f"📋 Registered '{data_config}' → {urdf_path}\nResolved: {resolved or 'NOT FOUND'}"}],
+ }
+
+ # --- Introspection ---
+
+ def get_features(self) -> dict[str, Any]:
+ if self._world is None or self._world._model is None:
+ return {"status": "error", "content": [{"text": "❌ No simulation."}]}
+
+ mj = _ensure_mujoco()
+ model = self._world._model
+
+ joint_names = [mj.mj_id2name(model, mj.mjtObj.mjOBJ_JOINT, i) for i in range(model.njnt)]
+ joint_names = [n for n in joint_names if n]
+ actuator_names = [mj.mj_id2name(model, mj.mjtObj.mjOBJ_ACTUATOR, i) for i in range(model.nu)]
+ actuator_names = [n for n in actuator_names if n]
+ camera_names = [mj.mj_id2name(model, mj.mjtObj.mjOBJ_CAMERA, i) for i in range(model.ncam)]
+ camera_names = [n for n in camera_names if n]
+
+ robots_info = {}
+ for rname, robot in self._world.robots.items():
+ robots_info[rname] = {
+ "joint_names": robot.joint_names,
+ "n_joints": len(robot.joint_names),
+ "n_actuators": len(robot.actuator_ids),
+ "data_config": robot.data_config,
+ "source": os.path.basename(robot.urdf_path),
+ }
+
+ features = {
+ "n_bodies": model.nbody,
+ "n_joints": model.njnt,
+ "n_actuators": model.nu,
+ "n_cameras": model.ncam,
+ "timestep": model.opt.timestep,
+ "joint_names": joint_names,
+ "actuator_names": actuator_names,
+ "camera_names": camera_names,
+ "robots": robots_info,
+ }
+
+ lines = [
+ "🔍 Simulation Features",
+ f"🦴 Joints ({model.njnt}): {', '.join(joint_names[:12])}{'...' if len(joint_names) > 12 else ''}",
+ f"⚡ Actuators ({model.nu}): {', '.join(actuator_names[:12])}{'...' if len(actuator_names) > 12 else ''}",
+ f"📷 Cameras ({model.ncam}): {', '.join(camera_names) if camera_names else 'none (free camera only)'}",
+ f"⏱️ Timestep: {model.opt.timestep}s ({1 / model.opt.timestep:.0f}Hz)",
+ ]
+ for rname, rinfo in robots_info.items():
+ lines.append(
+ f"🤖 {rname}: {rinfo['n_joints']} joints, {rinfo['n_actuators']} actuators ({rinfo['source']})"
+ )
+
+ return {
+ "status": "success",
+ "content": [{"text": "\n".join(lines)}, {"text": json.dumps({"features": features}, default=str)}],
+ }
+
+ # --- AgentTool Interface ---
+
+ @property
+ def tool_name(self) -> str:
+ return self.tool_name_str
+
+ @property
+ def tool_type(self) -> str:
+ return "simulation"
+
+ @property
+ def tool_spec(self) -> ToolSpec:
+ with open(_TOOL_SPEC_PATH) as f:
+ schema = json.load(f)
+ return {
+ "name": self.tool_name_str,
+ "description": (
+ "Programmatic MuJoCo simulation environment. Create worlds, add robots from URDF "
+ "(direct path or auto-resolve from data_config name), add objects, run VLA policies, "
+ "render cameras, record trajectories, domain randomize. "
+ "Same Policy ABC as real robot control — sim ↔ real with zero code changes. "
+ "Actions: create_world, load_scene, reset, get_state, destroy, "
+ "add_robot, remove_robot, list_robots, get_robot_state, "
+ "add_object, remove_object, move_object, list_objects, "
+ "add_camera, remove_camera, "
+ "run_policy, start_policy, stop_policy, "
+ "render, render_depth, get_contacts, "
+ "step, set_gravity, set_timestep, "
+ "randomize, "
+ "start_recording, stop_recording, get_recording_status, "
+ "open_viewer, close_viewer, "
+ "list_urdfs, register_urdf, get_features"
+ ),
+ "inputSchema": {"json": schema},
+ }
+
+ async def stream(
+ self, tool_use: ToolUse, invocation_state: dict[str, Any], **kwargs: Any
+ ) -> AsyncGenerator[ToolResultEvent, None]:
+ try:
+ tool_use_id = tool_use.get("toolUseId", "")
+ input_data = tool_use.get("input", {})
+ result = self._dispatch_action(input_data.get("action", ""), input_data)
+ yield ToolResultEvent({"toolUseId": tool_use_id, **result})
+ except Exception as e:
+ yield ToolResultEvent(
+ {
+ "toolUseId": tool_use.get("toolUseId", ""),
+ "status": "error",
+ "content": [{"text": f"❌ Sim error: {e}"}],
+ }
+ )
+
+ def _dispatch_action(self, action: str, d: dict[str, Any]) -> dict[str, Any]:
+ """Route action string to method via getattr.
+
+ Method names match action names directly (with a few aliases).
+ """
+ # Aliases for actions whose method names differ
+ _ALIASES = {
+ "list_urdfs": "list_urdfs_action",
+ "register_urdf": "register_urdf_action",
+ "stop_policy": "_stop_policy",
+ }
+
+ # Map input field names to method parameter names for physics actions
+ _FIELD_MAP = {
+ "checkpoint_name": "name",
+ "torque_vec": "torque",
+ }
+
+ method_name = _ALIASES.get(action, action)
+ method = getattr(self, method_name, None)
+
+ if method is None or action.startswith("_"):
+ return {"status": "error", "content": [{"text": f"❌ Unknown action: {action}"}]}
+
+ # Build kwargs from input dict, excluding 'action' itself
+ # Signatures are cached per method to avoid repeated introspection.
+ import inspect
+
+ cache = getattr(self, "_sig_cache", None)
+ if cache is None:
+ self._sig_cache = cache = {}
+ if method_name not in cache:
+ cache[method_name] = inspect.signature(method)
+ sig = cache[method_name]
+ # Apply field name remapping
+ remapped = dict(d)
+ for field_key, param_key in _FIELD_MAP.items():
+ if field_key in remapped and param_key not in remapped:
+ remapped[param_key] = remapped.pop(field_key)
+
+ kwargs = {}
+ for param_name, param in sig.parameters.items():
+ if param_name == "self":
+ continue
+ # Handle name/robot_name/body_name ambiguity in the input schema
+ if param_name == "name" and "name" not in remapped and "robot_name" in remapped:
+ kwargs["name"] = remapped["robot_name"]
+ elif param_name == "name" and "name" not in remapped and "checkpoint_name" in d:
+ kwargs["name"] = d["checkpoint_name"]
+ elif param_name == "robot_name" and "robot_name" not in remapped and "name" in remapped:
+ kwargs["robot_name"] = remapped["name"]
+ elif param_name in remapped:
+ kwargs[param_name] = remapped[param_name]
+ # Forward policy kwargs
+ elif param.kind == inspect.Parameter.VAR_KEYWORD:
+ for k in (
+ "policy_port",
+ "policy_host",
+ "model_path",
+ "server_address",
+ "policy_type",
+ "pretrained_name_or_path",
+ "device",
+ ):
+ if k in d:
+ kwargs[k] = d[k]
+
+ return method(**kwargs)
+
+ def _stop_policy(self, robot_name: str = "", **kwargs) -> dict[str, Any]:
+ if self._world and robot_name in self._world.robots:
+ self._world.robots[robot_name].policy_running = False
+ return {"status": "success", "content": [{"text": f"🛑 Stopped on '{robot_name}'"}]}
+ return {"status": "error", "content": [{"text": f"❌ '{robot_name}' not found."}]}
+
+ # --- Cleanup ---
+
+ def cleanup(self):
+ if hasattr(self, "mesh") and self.mesh:
+ self.mesh.stop()
+ if self._world:
+ for r in self._world.robots.values():
+ r.policy_running = False
+ self._world = None
+ self._close_viewer()
+ for renderer in getattr(self, "_renderers", {}).values():
+ try:
+ renderer.close()
+ except Exception:
+ pass
+ self._renderers.clear()
+ self._executor.shutdown(wait=False)
+ self._shutdown_event.set()
+
+ def __enter__(self):
+ return self
+
+ def __exit__(self, *exc):
+ self.cleanup()
+
+ def __del__(self):
+ try:
+ self.cleanup()
+ except Exception:
+ pass
diff --git a/strands_robots/simulation/mujoco/tool_spec.json b/strands_robots/simulation/mujoco/tool_spec.json
new file mode 100644
index 0000000..4147a4b
--- /dev/null
+++ b/strands_robots/simulation/mujoco/tool_spec.json
@@ -0,0 +1,351 @@
+{
+ "type": "object",
+ "properties": {
+ "action": {
+ "type": "string",
+ "description": "Action to perform",
+ "enum": [
+ "create_world",
+ "load_scene",
+ "reset",
+ "get_state",
+ "destroy",
+ "add_robot",
+ "remove_robot",
+ "list_robots",
+ "get_robot_state",
+ "add_object",
+ "remove_object",
+ "move_object",
+ "list_objects",
+ "add_camera",
+ "remove_camera",
+ "run_policy",
+ "start_policy",
+ "stop_policy",
+ "render",
+ "render_depth",
+ "get_contacts",
+ "step",
+ "set_gravity",
+ "set_timestep",
+ "randomize",
+ "start_recording",
+ "stop_recording",
+ "get_recording_status",
+ "open_viewer",
+ "close_viewer",
+ "list_urdfs",
+ "register_urdf",
+ "get_features",
+ "replay_episode",
+ "eval_policy",
+ "save_state",
+ "load_state",
+ "apply_force",
+ "raycast",
+ "multi_raycast",
+ "get_jacobian",
+ "get_energy",
+ "get_mass_matrix",
+ "inverse_dynamics",
+ "get_body_state",
+ "set_joint_positions",
+ "set_joint_velocities",
+ "get_sensor_data",
+ "set_body_properties",
+ "set_geom_properties",
+ "get_contact_forces",
+ "forward_kinematics",
+ "get_total_mass",
+ "export_xml"
+ ]
+ },
+ "scene_path": {
+ "type": "string",
+ "description": "Path to MJCF/URDF scene file"
+ },
+ "timestep": {
+ "type": "number"
+ },
+ "gravity": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ }
+ },
+ "ground_plane": {
+ "type": "boolean"
+ },
+ "urdf_path": {
+ "type": "string",
+ "description": "Path to URDF/MJCF file"
+ },
+ "robot_name": {
+ "type": "string"
+ },
+ "data_config": {
+ "type": "string",
+ "description": "Data config name (auto-resolves URDF)"
+ },
+ "name": {
+ "type": "string",
+ "description": "Object/camera name"
+ },
+ "shape": {
+ "type": "string",
+ "enum": [
+ "box",
+ "sphere",
+ "cylinder",
+ "capsule",
+ "mesh",
+ "plane"
+ ]
+ },
+ "position": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ }
+ },
+ "orientation": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ }
+ },
+ "size": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ }
+ },
+ "color": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ }
+ },
+ "mass": {
+ "type": "number"
+ },
+ "is_static": {
+ "type": "boolean"
+ },
+ "mesh_path": {
+ "type": "string"
+ },
+ "target": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ },
+ "description": "Camera target point"
+ },
+ "fov": {
+ "type": "number",
+ "description": "Camera field of view"
+ },
+ "width": {
+ "type": "integer"
+ },
+ "height": {
+ "type": "integer"
+ },
+ "policy_provider": {
+ "type": "string",
+ "description": "Policy provider name (e.g. groot, lerobot_async, lerobot_local, dreamgen, mock)"
+ },
+ "instruction": {
+ "type": "string"
+ },
+ "duration": {
+ "type": "number"
+ },
+ "policy_port": {
+ "type": "integer"
+ },
+ "policy_host": {
+ "type": "string"
+ },
+ "model_path": {
+ "type": "string"
+ },
+ "action_horizon": {
+ "type": "integer"
+ },
+ "control_frequency": {
+ "type": "number"
+ },
+ "camera_name": {
+ "type": "string"
+ },
+ "n_steps": {
+ "type": "integer"
+ },
+ "output_path": {
+ "type": "string",
+ "description": "Trajectory/video export path"
+ },
+ "fps": {
+ "type": "integer",
+ "description": "Video frames per second (for run_policy record_video)"
+ },
+ "pretrained_name_or_path": {
+ "type": "string",
+ "description": "HuggingFace model ID for lerobot_local"
+ },
+ "randomize_colors": {
+ "type": "boolean"
+ },
+ "randomize_lighting": {
+ "type": "boolean"
+ },
+ "randomize_physics": {
+ "type": "boolean"
+ },
+ "randomize_positions": {
+ "type": "boolean"
+ },
+ "position_noise": {
+ "type": "number"
+ },
+ "seed": {
+ "type": "integer",
+ "description": "Random seed"
+ },
+ "repo_id": {
+ "type": "string",
+ "description": "HuggingFace dataset repo ID"
+ },
+ "push_to_hub": {
+ "type": "boolean",
+ "description": "Auto-push dataset to HuggingFace Hub on stop_recording"
+ },
+ "vcodec": {
+ "type": "string",
+ "description": "Video codec for dataset recording (h264, hevc, libsvtav1)"
+ },
+ "task": {
+ "type": "string",
+ "description": "Task description for dataset recording"
+ },
+ "episode": {
+ "type": "integer",
+ "description": "Episode index for replay_episode"
+ },
+ "root": {
+ "type": "string",
+ "description": "Local dataset root directory"
+ },
+ "speed": {
+ "type": "number",
+ "description": "Replay speed multiplier (1.0 = original)"
+ },
+ "n_episodes": {
+ "type": "integer",
+ "description": "Number of eval episodes"
+ },
+ "max_steps": {
+ "type": "integer",
+ "description": "Max steps per eval episode"
+ },
+ "success_fn": {
+ "type": "string",
+ "description": "Success function ('contact')"
+ },
+ "fast_mode": {
+ "type": "boolean",
+ "description": "Skip sleep between actions for faster data collection"
+ },
+ "body_name": {
+ "type": "string",
+ "description": "Target body name"
+ },
+ "site_name": {
+ "type": "string",
+ "description": "Site name for Jacobian"
+ },
+ "geom_name": {
+ "type": "string",
+ "description": "Geom name"
+ },
+ "geom_id": {
+ "type": "integer",
+ "description": "Geom ID (alternative to geom_name)"
+ },
+ "force": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ },
+ "description": "Force vector [fx, fy, fz] in Newtons"
+ },
+ "torque_vec": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ },
+ "description": "Torque vector [tx, ty, tz] in N\u00b7m"
+ },
+ "point": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ },
+ "description": "Point of force application [x, y, z]"
+ },
+ "origin": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ },
+ "description": "Ray origin [x, y, z]"
+ },
+ "direction": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ },
+ "description": "Ray direction [dx, dy, dz]"
+ },
+ "directions": {
+ "type": "array",
+ "items": {
+ "type": "array",
+ "items": {
+ "type": "number"
+ }
+ },
+ "description": "Multiple ray directions for multi_raycast"
+ },
+ "exclude_body": {
+ "type": "integer",
+ "description": "Body ID to exclude from raycast (-1=none)"
+ },
+ "include_static": {
+ "type": "boolean",
+ "description": "Include static geoms in raycast"
+ },
+ "positions": {
+ "type": "object",
+ "description": "Joint name \u2192 position mapping for set_joint_positions"
+ },
+ "velocities": {
+ "type": "object",
+ "description": "Joint name \u2192 velocity mapping for set_joint_velocities"
+ },
+ "sensor_name": {
+ "type": "string",
+ "description": "Specific sensor name (or omit for all)"
+ },
+ "checkpoint_name": {
+ "type": "string",
+ "description": "Named checkpoint for save_state/load_state"
+ }
+ },
+ "required": [
+ "action"
+ ]
+}
\ No newline at end of file
diff --git a/strands_robots/tools/download_assets.py b/strands_robots/tools/download_assets.py
new file mode 100644
index 0000000..0ce4881
--- /dev/null
+++ b/strands_robots/tools/download_assets.py
@@ -0,0 +1,493 @@
+"""Download robot model assets via ``robot_descriptions`` or custom GitHub repos.
+
+Uses the `robot_descriptions `_
+package (recommended by MuJoCo Menagerie) as the primary download backend.
+Falls back to a shallow ``git clone`` when the package is not installed.
+
+Assets are cached in ``~/.strands_robots/assets/`` (override with
+``STRANDS_ASSETS_DIR``). Install the optional dependency::
+
+ pip install strands-robots[sim] # includes robot_descriptions
+
+CLI::
+
+ python -m strands_robots.tools.download_assets
+ python -m strands_robots.tools.download_assets so100 panda unitree_g1
+ python -m strands_robots.tools.download_assets --category arm
+ python -m strands_robots.tools.download_assets --list
+
+Agent::
+
+ from strands_robots.tools.download_assets import download_assets
+ agent = Agent(tools=[download_assets])
+ agent("Download the SO-100 and Panda robot assets")
+"""
+
+import argparse
+import importlib
+import logging
+import os
+import re
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Any
+
+try:
+ from strands.tools.decorator import tool
+except ImportError:
+
+ def tool(f):
+ return f
+
+
+from strands_robots.assets import format_robot_table, get_search_paths
+from strands_robots.registry import get_robot
+from strands_robots.registry import list_robots as registry_list_robots
+from strands_robots.registry import resolve_name as resolve_robot_name
+
+logger = logging.getLogger(__name__)
+
+MENAGERIE_REPO = "https://github.com/google-deepmind/mujoco_menagerie.git"
+
+
+# ── robot_descriptions integration ────────────────────────────────────
+
+
+def _robot_descriptions_available() -> bool:
+ """Check if ``robot_descriptions`` is installed."""
+ try:
+ import robot_descriptions # noqa: F401
+
+ return True
+ except ImportError:
+ return False
+
+
+def _resolve_robot_descriptions_module(name: str, info: dict) -> str | None:
+ """Resolve the ``robot_descriptions`` module name for a robot.
+
+ Uses the ``robot_descriptions_module`` field from the registry (O(1)),
+ with a lightweight naming-convention fallback for unregistered robots.
+
+ Args:
+ name: Canonical robot name.
+ info: Robot registry entry.
+
+ Returns:
+ Module name (e.g. ``panda_mj_description``) or ``None``.
+ """
+ # Primary: explicit registry entry (preferred, O(1))
+ module_name = info.get("asset", {}).get("robot_descriptions_module")
+ if module_name:
+ return module_name
+
+ # Fallback: try common naming conventions (max 3 imports)
+ asset_dir = info.get("asset", {}).get("dir", "")
+ candidates = [
+ f"{asset_dir}_mj_description",
+ f"{name}_mj_description",
+ f"{name}_description",
+ ]
+ for candidate in candidates:
+ if not re.match(r"^[a-z0-9_]+$", candidate):
+ continue
+ try:
+ importlib.import_module(f"robot_descriptions.{candidate}")
+ logger.warning(
+ "Resolved '%s' via naming heuristic → '%s'. "
+ "Consider adding 'robot_descriptions_module' to the registry.",
+ name,
+ candidate,
+ )
+ return candidate
+ except ImportError:
+ continue
+
+ return None
+
+
+# ── Helpers ───────────────────────────────────────────────────────────
+
+
+def get_user_assets_dir() -> Path:
+ """Get user-level asset cache directory."""
+ custom = os.getenv("STRANDS_ASSETS_DIR")
+ directory = Path(custom) if custom else Path.home() / ".strands_robots" / "assets"
+ directory.mkdir(parents=True, exist_ok=True)
+ return directory
+
+
+def _safe_join(base: Path, untrusted: str) -> Path:
+ """Join *base* with an untrusted relative path, rejecting traversal."""
+ joined = Path(os.path.normpath(base / untrusted))
+ base_norm = Path(os.path.normpath(base))
+ if not (joined == base_norm or str(joined).startswith(str(base_norm) + os.sep)):
+ raise ValueError(f"Path traversal blocked: {untrusted!r} escapes {base}")
+ return joined
+
+
+def _needs_download(name: str, info: dict, force: bool = False) -> bool:
+ """Return *True* if a robot's mesh files are missing."""
+ asset = info.get("asset", {})
+ if not asset:
+ return False
+
+ xml_file, asset_dir = asset["model_xml"], asset["dir"]
+
+ for search_dir in get_search_paths():
+ model_path = search_dir / asset_dir / xml_file
+ if not model_path.exists():
+ continue
+ try:
+ content = model_path.read_text()
+ mesh_files = re.findall(r'file="([^"]+\.(?:stl|STL|obj|OBJ|msh))"', content)
+ if not mesh_files:
+ return False
+ meshdir_match = re.search(r'meshdir="([^"]*)"', content)
+ meshdir = meshdir_match.group(1) if meshdir_match else ""
+ for mesh in mesh_files[:3]:
+ if not (model_path.parent / meshdir / mesh).exists():
+ return True
+ return force
+ except Exception:
+ return True
+
+ return True
+
+
+def _get_source(info: dict) -> dict:
+ """Get download source for a robot. Defaults to ``menagerie``."""
+ source = info.get("asset", {}).get("source", {})
+ return source if source else {"type": "menagerie"}
+
+
+def _shallow_clone(repo_url: str, dest: str, *, timeout: int = 120) -> None:
+ """Shallow-clone *repo_url* into *dest*. Raises on failure."""
+ logger.info("Cloning %s (this may take a moment)...", repo_url)
+ subprocess.run(
+ ["git", "clone", "--depth", "1", repo_url, dest],
+ check=True,
+ capture_output=True,
+ timeout=timeout,
+ )
+
+
+def _copy_and_clean(src: Path, dst: Path) -> None:
+ """Copy *src* tree to *dst* and remove non-essential files."""
+ shutil.copytree(str(src), str(dst), dirs_exist_ok=True)
+ for pattern in ("README.md", "LICENSE", "CHANGELOG.md", "*.png", "*.jpg", ".git*"):
+ for path in dst.glob(pattern):
+ if path.is_file():
+ path.unlink()
+ elif path.is_dir():
+ shutil.rmtree(str(path), ignore_errors=True)
+
+
+# ── Download backends ─────────────────────────────────────────────────
+
+
+def _download_via_robot_descriptions(robots: dict[str, dict], dest_dir: Path) -> dict[str, str]:
+ """Download robots using the ``robot_descriptions`` package.
+
+ Imports only the specific module for each robot (O(1) per robot),
+ using the ``robot_descriptions_module`` field from the registry.
+ The import triggers the upstream clone on first use, then we symlink
+ ``PACKAGE_PATH`` into our asset cache.
+ """
+ results: dict[str, str] = {}
+ if not robots:
+ return results
+
+ for name, info in robots.items():
+ asset_dir = info["asset"]["dir"]
+ module_name = _resolve_robot_descriptions_module(name, info)
+ if module_name is None:
+ results[name] = "skipped: no robot_descriptions module found"
+ continue
+ if not re.match(r"^[a-z0-9_]+$", module_name):
+ results[name] = f"skipped: invalid module name: {module_name}"
+ continue
+
+ try:
+ mod = importlib.import_module(f"robot_descriptions.{module_name}")
+ package_path = Path(mod.PACKAGE_PATH)
+ if not package_path.exists():
+ results[name] = f"failed: PACKAGE_PATH missing: {package_path}"
+ continue
+
+ dst = _safe_join(dest_dir, asset_dir)
+ if dst.is_symlink() and dst.resolve() == package_path.resolve():
+ results[name] = "downloaded"
+ continue
+ if dst.exists() or dst.is_symlink():
+ dst.unlink() if dst.is_symlink() else shutil.rmtree(str(dst))
+
+ try:
+ dst.symlink_to(package_path)
+ except OSError:
+ shutil.copytree(str(package_path), str(dst), dirs_exist_ok=True)
+
+ results[name] = "downloaded"
+ except Exception as exc:
+ results[name] = f"failed: {exc}"
+ logger.warning("robot_descriptions failed for %s: %s", name, exc)
+
+ return results
+
+
+def _download_via_git(robots: dict[str, dict], dest_dir: Path) -> dict[str, str]:
+ """Fallback: shallow-clone Menagerie and copy robot directories."""
+ results: dict[str, str] = {}
+ if not robots:
+ return results
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ clone_dir = os.path.join(tmpdir, "mujoco_menagerie")
+ try:
+ _shallow_clone(MENAGERIE_REPO, clone_dir)
+ except (subprocess.TimeoutExpired, subprocess.CalledProcessError) as exc:
+ reason = "timeout" if isinstance(exc, subprocess.TimeoutExpired) else str(exc)[:100]
+ return {n: f"failed: git clone {reason}" for n in robots}
+
+ for name, info in robots.items():
+ asset_dir = info["asset"]["dir"]
+ src = _safe_join(Path(clone_dir), asset_dir)
+ if not src.exists():
+ results[name] = f"failed: {asset_dir} not in menagerie"
+ continue
+ try:
+ _copy_and_clean(src, _safe_join(dest_dir, asset_dir))
+ results[name] = "downloaded"
+ except Exception as exc:
+ results[name] = f"failed: {exc}"
+
+ return results
+
+
+def _download_from_github(name: str, info: dict, dest_dir: Path) -> str:
+ """Download a robot from a custom GitHub repo (``asset.source``)."""
+ source = info["asset"]["source"]
+ repo = source["repo"]
+ if not re.match(r"^[a-zA-Z0-9_.-]+/[a-zA-Z0-9_.-]+$", repo):
+ return f"failed: invalid repo format: {repo}"
+
+ subdir = source.get("subdir", "")
+ asset_dir = info["asset"]["dir"]
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ clone_dir = os.path.join(tmpdir, "repo")
+ try:
+ _shallow_clone(f"https://github.com/{repo}.git", clone_dir)
+ except (subprocess.TimeoutExpired, subprocess.CalledProcessError) as exc:
+ reason = "timeout" if isinstance(exc, subprocess.TimeoutExpired) else str(exc)[:100]
+ return f"failed: git clone {reason}"
+
+ src = Path(clone_dir) / subdir if subdir else Path(clone_dir)
+ if not src.exists():
+ return f"failed: subdir '{subdir}' not found in {repo}"
+
+ dst = _safe_join(dest_dir, asset_dir)
+ try:
+ _copy_and_clean(src, dst)
+ # Copy bundled XML files so mesh paths resolve
+ bundled_dir = Path(__file__).parent.parent / "assets" / asset_dir
+ if bundled_dir.exists():
+ for xml_file in bundled_dir.glob("**/*.xml"):
+ target = dst / xml_file.relative_to(bundled_dir)
+ target.parent.mkdir(parents=True, exist_ok=True)
+ if not target.exists():
+ shutil.copy2(str(xml_file), str(target))
+ return "downloaded"
+ except Exception as exc:
+ return f"failed: {exc}"
+
+
+# ── Orchestrator ──────────────────────────────────────────────────────
+
+
+def download_robots(
+ names: list[str] | None = None,
+ category: str | None = None,
+ force: bool = False,
+) -> dict[str, Any]:
+ """Download robot model assets from their respective sources.
+
+ Strategy (in order of preference):
+ 1. ``robot_descriptions`` package — recommended by MuJoCo Menagerie.
+ 2. Shallow ``git clone`` fallback for Menagerie robots.
+ 3. Custom GitHub repos for non-Menagerie robots.
+
+ Args:
+ names: Robot names to download (``None`` = all sim robots).
+ category: Filter by category (arm, humanoid, mobile, …).
+ force: Re-download even if present.
+ """
+ dest_dir = get_user_assets_dir()
+ all_sim = {r["name"]: get_robot(r["name"]) for r in registry_list_robots(mode="sim")}
+
+ # Resolve requested robots
+ if names:
+ robots = {}
+ for name in names:
+ canonical = resolve_robot_name(name)
+ if canonical in all_sim:
+ robots[canonical] = all_sim[canonical]
+ else:
+ logger.warning("Unknown robot: %s (resolved: %s)", name, canonical)
+ elif category:
+ robots = {n: i for n, i in all_sim.items() if i and i.get("category") == category}
+ else:
+ robots = {n: i for n, i in all_sim.items() if i}
+
+ if not robots:
+ return {"downloaded": 0, "skipped": 0, "failed": 0, "message": "No matching robots found."}
+
+ # Partition: needs download vs already present
+ to_download, skipped = {}, []
+ for name, info in robots.items():
+ if _needs_download(name, info, force):
+ to_download[name] = info
+ else:
+ skipped.append(name)
+
+ if not to_download:
+ return {
+ "downloaded": 0,
+ "skipped": len(skipped),
+ "failed": 0,
+ "skipped_names": skipped,
+ "message": f"All {len(robots)} robots already have assets. Use force=True to re-download.",
+ }
+
+ # Partition by source type
+ menagerie_robots, github_robots = {}, {}
+ for name, info in to_download.items():
+ source = _get_source(info)
+ bucket = github_robots if source["type"] == "github" else menagerie_robots
+ bucket[name] = info
+
+ # Download Menagerie robots (robot_descriptions → git fallback)
+ results: dict[str, str] = {}
+ if menagerie_robots:
+ if _robot_descriptions_available():
+ results.update(_download_via_robot_descriptions(menagerie_robots, dest_dir))
+ # Retry failures with git clone
+ retry = {
+ n: menagerie_robots[n] for n, r in results.items() if r.startswith("failed") or r.startswith("skipped")
+ }
+ if retry:
+ results.update(_download_via_git(retry, dest_dir))
+ else:
+ results.update(_download_via_git(menagerie_robots, dest_dir))
+
+ # Download custom GitHub robots
+ for name, info in github_robots.items():
+ results[name] = _download_from_github(name, info, dest_dir)
+
+ downloaded = [n for n, r in results.items() if r == "downloaded"]
+ failed = {n: r for n, r in results.items() if r != "downloaded"}
+ method = "robot_descriptions" if _robot_descriptions_available() else "git clone"
+
+ return {
+ "downloaded": len(downloaded),
+ "skipped": len(skipped),
+ "failed": len(failed),
+ "downloaded_names": downloaded,
+ "skipped_names": skipped,
+ "failed_names": list(failed),
+ "failed_details": failed,
+ "assets_dir": str(dest_dir),
+ "method": method,
+ "message": f"{len(downloaded)} downloaded ({method}), {len(skipped)} already present, {len(failed)} failed.",
+ }
+
+
+# ── Agent tool ────────────────────────────────────────────────────────
+
+
+@tool
+def download_assets(
+ action: str = "download",
+ robots: str = None,
+ category: str = None,
+ force: bool = False,
+) -> dict[str, Any]:
+ """Download and manage robot model assets (MJCF XML + meshes).
+
+ Uses ``robot_descriptions`` (recommended by MuJoCo Menagerie) with git
+ clone fallback. Assets cached in ``~/.strands_robots/assets/``.
+
+ Args:
+ action: ``download`` | ``list`` | ``status``
+ robots: Comma-separated names (e.g. ``so100,panda``). Omit for all.
+ category: Filter: arm, bimanual, hand, humanoid, mobile, mobile_manip
+ force: Re-download even if present
+ """
+ try:
+ if action == "list":
+ return {"status": "success", "content": [{"text": f"🤖 Available Robots:\n\n{format_robot_table()}"}]}
+
+ if action == "status":
+ from strands_robots.assets import list_available_robots
+
+ robots_info = list_available_robots()
+ available = sum(1 for r in robots_info if r["available"])
+ lines = [f"📊 {available} available, {len(robots_info) - available} missing"]
+ lines.extend(
+ f" {'✅' if r['available'] else '❌'} {r['name']:<20s} {r['category']:<12s} {r['description']}"
+ for r in robots_info
+ )
+ lines.append(f"\n📁 Cache: {get_user_assets_dir()}")
+ return {"status": "success", "content": [{"text": "\n".join(lines)}]}
+
+ if action == "download":
+ robot_names = [r.strip() for r in robots.split(",") if r.strip()] if robots else None
+ result = download_robots(names=robot_names, category=category, force=force)
+ parts = [
+ f"📦 Downloaded: {result['downloaded']}, Skipped: {result['skipped']}, Failed: {result['failed']}",
+ f"Method: {result.get('method', '?')}",
+ ]
+ if result.get("failed_details"):
+ parts.extend(f" ❌ {n}: {r}" for n, r in result["failed_details"].items())
+ parts.append(f"📁 Assets: {result.get('assets_dir', '?')}")
+ return {"status": "success", "content": [{"text": "\n".join(parts)}]}
+
+ return {"status": "error", "content": [{"text": f"Unknown action: {action}. Valid: download, list, status"}]}
+
+ except Exception as exc:
+ logger.error("download_assets error: %s", exc)
+ return {"status": "error", "content": [{"text": f"❌ Error: {exc}"}]}
+
+
+# ── CLI ───────────────────────────────────────────────────────────────
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Download robot assets (robot_descriptions / git clone)")
+ parser.add_argument("robots", nargs="*", help="Robot names (default: all)")
+ parser.add_argument(
+ "--category", "-c", choices=["arm", "bimanual", "hand", "humanoid", "mobile", "mobile_manip", "expressive"]
+ )
+ parser.add_argument("--force", "-f", action="store_true")
+ parser.add_argument("--list", "-l", action="store_true")
+ parser.add_argument("--status", "-s", action="store_true")
+ args = parser.parse_args()
+
+ if args.list:
+ print(format_robot_table())
+ return
+ if args.status:
+ for content in download_assets(action="status").get("content", []):
+ print(content.get("text", ""))
+ return
+
+ result = download_robots(names=args.robots or None, category=args.category, force=args.force)
+ print(result["message"])
+ for name, reason in result.get("failed_details", {}).items():
+ print(f" ❌ {name}: {reason}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/test.py b/test.py
deleted file mode 100644
index 030ac32..0000000
--- a/test.py
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/usr/bin/env python3
-from strands import Agent
-from strands_tools import shell, slack
-from strands_robots import Robot, gr00t_inference, lerobot_camera, pose_tool
-
-robot = Robot(
- tool_name="orange_arm",
- robot="so101_follower",
- cameras={
- "wrist": {"type": "opencv", "index_or_path": "/dev/video0", "fps": 15, "fourcc": "MJPG"},
- "front": {"type": "opencv", "index_or_path": "/dev/video2", "fps": 15, "fourcc": "MJPG"},
- },
- port="/dev/ttyACM0",
- data_config="so100_dualcam",
-)
-
-agent = Agent(
- tools=[shell, robot, gr00t_inference, lerobot_camera, pose_tool, slack],
- load_tools_from_directory=True,
-)
-
-agent.tool.gr00t_inference(
- action="start",
- checkpoint_path="/data/checkpoints/gr00t-wave/checkpoint-300000", # Model -> https://huggingface.co/cagataydev/gr00t-wave
- port=5555,
- data_config="so100_dualcam",
- embodiment_tag="new_embodiment",
-)
-
-# agent("Use the orange_arm robot to wave the arm using GR00T policy on port 8000")
-
-# agent.tool.slack(action="start_socket_mode", agent=agent)
-# agent.tool.gr00t_inference(action="stop", port=8000)
-
-while True:
- agent(input("\n# "))
diff --git a/tests/test_factory.py b/tests/test_factory.py
new file mode 100644
index 0000000..068b737
--- /dev/null
+++ b/tests/test_factory.py
@@ -0,0 +1,148 @@
+"""Tests for strands_robots.factory — Robot(), list_robots()."""
+
+import os
+
+import pytest
+
+from strands_robots.factory import Robot, _auto_detect_mode, list_robots
+from strands_robots.registry import (
+ get_robot,
+ list_aliases,
+ resolve_name,
+)
+from strands_robots.registry import list_robots as registry_list_robots
+
+
+class TestResolveNames:
+ def test_canonical(self):
+ assert resolve_name("so100") == "so100"
+
+ def test_alias(self):
+ assert resolve_name("franka") == "panda"
+ assert resolve_name("g1") == "unitree_g1"
+ assert resolve_name("h1") == "unitree_h1"
+
+ def test_case_insensitive(self):
+ assert resolve_name("SO100") == "so100"
+ assert resolve_name("Panda") == "panda"
+
+ def test_hyphen_to_underscore(self):
+ assert resolve_name("reachy-mini") == "reachy_mini"
+
+
+class TestListRobots:
+ def test_list_all(self):
+ robots = list_robots("all")
+ assert len(robots) > 0
+ names = [r["name"] for r in robots]
+ assert "so100" in names
+ assert "panda" in names
+
+ def test_list_sim(self):
+ robots = list_robots("sim")
+ for r in robots:
+ assert r["has_sim"] is True
+
+ def test_list_real(self):
+ robots = list_robots("real")
+ for r in robots:
+ assert r["has_real"] is True
+
+ def test_list_both(self):
+ robots = list_robots("both")
+ for r in robots:
+ assert r["has_sim"] is True
+ assert r["has_real"] is True
+
+ def test_robot_has_fields(self):
+ robots = list_robots()
+ for r in robots:
+ assert "name" in r
+ assert "description" in r
+ assert "has_sim" in r
+ assert "has_real" in r
+
+
+class TestRobotRegistry:
+ def test_so100_exists(self):
+ info = get_robot("so100")
+ assert info is not None
+ assert "asset" in info
+ assert info["asset"]["dir"] == "trs_so_arm100"
+
+ def test_all_aliases_point_to_valid_robots(self):
+ aliases = list_aliases()
+ for alias, canonical in aliases.items():
+ info = get_robot(canonical)
+ assert info is not None, f"Alias '{alias}' points to unknown robot '{canonical}'"
+
+ def test_robot_count(self):
+ """Ensure we have a reasonable number of robots."""
+ robots = registry_list_robots()
+ assert len(robots) >= 30
+
+ def test_all_robots_have_description(self):
+ robots = registry_list_robots()
+ for r in robots:
+ assert "description" in r, f"Robot '{r['name']}' missing description"
+ assert len(r["description"]) > 0
+
+
+class TestAutoDetectMode:
+ def test_defaults_to_sim(self):
+ """No hardware plugged in → sim."""
+ assert _auto_detect_mode("so100") == "sim"
+
+ def test_env_override_real(self):
+ os.environ["STRANDS_ROBOT_MODE"] = "real"
+ try:
+ assert _auto_detect_mode("so100") == "real"
+ finally:
+ del os.environ["STRANDS_ROBOT_MODE"]
+
+ def test_env_override_sim(self):
+ os.environ["STRANDS_ROBOT_MODE"] = "sim"
+ try:
+ assert _auto_detect_mode("so100") == "sim"
+ finally:
+ del os.environ["STRANDS_ROBOT_MODE"]
+
+ def test_env_override_case_insensitive(self):
+ os.environ["STRANDS_ROBOT_MODE"] = "REAL"
+ try:
+ # .lower() normalizes to "real" — should match
+ mode = _auto_detect_mode("so100")
+ assert mode == "real" # .lower() normalizes REAL → real
+ finally:
+ del os.environ["STRANDS_ROBOT_MODE"]
+
+
+class TestRobotFactory:
+ def test_robot_is_callable(self):
+ """Robot is a factory function, not a class."""
+ import inspect
+
+ assert callable(Robot)
+ assert not inspect.isclass(Robot)
+
+ def test_unknown_backend_raises(self):
+ with pytest.raises(NotImplementedError, match="not yet implemented"):
+ Robot("so100", mode="sim", backend="isaac")
+
+ def test_newton_not_implemented(self):
+ with pytest.raises(NotImplementedError, match="not yet implemented"):
+ Robot("so100", mode="sim", backend="newton")
+
+ def test_sim_with_urdf_path(self):
+ """Robot() with explicit urdf_path should work (if file exists)."""
+ # We don't have a real URDF here, but verify the param is accepted
+ with pytest.raises(RuntimeError):
+ Robot("test_bot", mode="sim", urdf_path="/nonexistent/robot.xml")
+
+ def test_import_from_top_level(self):
+ """Robot and list_robots importable from strands_robots."""
+ from strands_robots import Robot as R
+ from strands_robots import list_robots as lr
+
+ assert R is Robot
+ assert lr is list_robots
diff --git a/tests/test_lerobot_local.py b/tests/test_lerobot_local.py
index e1113d7..20a52ee 100644
--- a/tests/test_lerobot_local.py
+++ b/tests/test_lerobot_local.py
@@ -575,12 +575,16 @@ def test_numpy_floating_state(self):
np.testing.assert_allclose(batch["observation.state"][0].numpy(), [1.5, 2.5], atol=1e-5)
def test_state_padded_to_expected_dim(self):
- """State dimension mismatch should raise ValueError (fail-fast)."""
+ """State dimension mismatch should auto-pad (not raise)."""
policy = _make_loaded_policy(state_dim=4, include_images=False)
policy.set_robot_state_keys(["a", "b"])
observation = {"a": 1.0, "b": 2.0}
- with pytest.raises(ValueError, match="State dimension mismatch"):
- policy._build_batch_from_strands_format(observation, {})
+ # After bug fix: auto-pads with zeros instead of raising
+ batch = policy._build_batch_from_strands_format(observation, {})
+ state = batch["observation.state"][0].numpy()
+ assert len(state) == 4
+ np.testing.assert_allclose(state[:2], [1.0, 2.0], atol=1e-5)
+ np.testing.assert_allclose(state[2:], [0.0, 0.0], atol=1e-5)
def test_empty_state_keys_raises(self):
"""Empty robot_state_keys should raise ValueError."""
diff --git a/tests/test_mujoco_e2e.py b/tests/test_mujoco_e2e.py
new file mode 100644
index 0000000..c09cb0c
--- /dev/null
+++ b/tests/test_mujoco_e2e.py
@@ -0,0 +1,269 @@
+"""End-to-end MuJoCo simulation test with Policy ABC.
+
+Tests the full observe → policy → act → step → render pipeline
+without requiring strands SDK or lerobot — just mujoco + numpy.
+
+Run: python -m pytest tests/test_mujoco_e2e.py -v
+"""
+
+import asyncio
+import os
+import shutil
+import tempfile
+
+import numpy as np
+import pytest
+
+# Skip entire module if mujoco not installed
+mj = pytest.importorskip("mujoco")
+
+
+def _has_opengl() -> bool:
+ """Check if OpenGL rendering is available."""
+ try:
+ model = mj.MjModel.from_xml_string("")
+ renderer = mj.Renderer(model, height=1, width=1)
+ del renderer
+ return True
+ except Exception:
+ return False
+
+
+requires_gl = pytest.mark.skipif(
+ not _has_opengl(),
+ reason="No OpenGL context available (headless environment without EGL/OSMesa)",
+)
+
+
+from strands_robots.policies import MockPolicy # noqa: E402
+from strands_robots.simulation.base import SimulationBackend # noqa: E402
+from strands_robots.simulation.models import SimObject, SimRobot, SimStatus, SimWorld # noqa: E402
+
+# ── Fixtures ──
+
+ROBOT_XML = """
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+"""
+
+
+@pytest.fixture
+def sim_env():
+ """Create a MuJoCo model+data from test XML."""
+ tmpdir = tempfile.mkdtemp()
+ xml_path = os.path.join(tmpdir, "test_arm.xml")
+ with open(xml_path, "w") as f:
+ f.write(ROBOT_XML)
+
+ model = mj.MjModel.from_xml_path(xml_path)
+ data = mj.MjData(model)
+
+ yield model, data
+
+ shutil.rmtree(tmpdir, ignore_errors=True)
+
+
+JOINT_NAMES = ["shoulder_pan", "shoulder_lift", "elbow"]
+
+
+def read_joints(model, data):
+ obs = {}
+ for jname in JOINT_NAMES:
+ jid = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, jname)
+ obs[jname] = float(data.qpos[model.jnt_qposadr[jid]])
+ return obs
+
+
+def apply_action(model, data, action_dict):
+ for key, val in action_dict.items():
+ act_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_ACTUATOR, f"{key}_act")
+ if act_id >= 0:
+ data.ctrl[act_id] = val
+
+
+# ── Tests ──
+
+
+class TestSimulationBase:
+ def test_abc_has_required_methods(self):
+ required = [
+ "create_world",
+ "destroy",
+ "reset",
+ "step",
+ "get_state",
+ "add_robot",
+ "remove_robot",
+ "add_object",
+ "remove_object",
+ "get_observation",
+ "send_action",
+ "render",
+ ]
+ for method in required:
+ assert hasattr(SimulationBackend, method)
+
+ def test_shared_dataclasses(self):
+ w = SimWorld()
+ assert w.timestep == 0.002
+ assert w.gravity == [0.0, 0.0, -9.81]
+ assert w.status == SimStatus.IDLE
+
+ r = SimRobot(name="test", urdf_path="/tmp/test.urdf")
+ assert r.joint_names == []
+
+ o = SimObject(name="cube", shape="box")
+ assert o.mass == 0.1
+
+
+class TestMuJoCoPhysics:
+ def test_step_advances_time(self, sim_env):
+ model, data = sim_env
+ assert data.time == 0.0
+ for _ in range(100):
+ mj.mj_step(model, data)
+ assert data.time == pytest.approx(0.2, abs=1e-6)
+
+ def test_position_actuators_move_joints(self, sim_env):
+ model, data = sim_env
+ data.ctrl[0] = 1.0 # shoulder_pan target
+ for _ in range(1000):
+ mj.mj_step(model, data)
+ obs = read_joints(model, data)
+ assert abs(obs["shoulder_pan"] - 1.0) < 0.15
+
+ def test_contacts_detected(self, sim_env):
+ model, data = sim_env
+ for _ in range(100):
+ mj.mj_step(model, data)
+ assert data.ncon > 0 # cube on ground
+
+ def test_reset_zeros_time(self, sim_env):
+ model, data = sim_env
+ for _ in range(100):
+ mj.mj_step(model, data)
+ mj.mj_resetData(model, data)
+ assert data.time == 0.0
+
+
+@requires_gl
+class TestMuJoCoRendering:
+ def test_render_rgb(self, sim_env):
+ model, data = sim_env
+ mj.mj_forward(model, data)
+ renderer = mj.Renderer(model, height=240, width=320)
+ cam_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_CAMERA, "front")
+ renderer.update_scene(data, camera=cam_id)
+ img = renderer.render()
+ assert img.shape == (240, 320, 3)
+ assert img.dtype == np.uint8
+ assert img.max() > 0
+ del renderer
+
+ def test_render_depth(self, sim_env):
+ model, data = sim_env
+ mj.mj_forward(model, data)
+ renderer = mj.Renderer(model, height=120, width=160)
+ renderer.update_scene(data)
+ renderer.enable_depth_rendering()
+ depth = renderer.render()
+ renderer.disable_depth_rendering()
+ assert depth.shape == (120, 160)
+ assert depth.max() > 0
+ del renderer
+
+
+class TestMockPolicyLoop:
+ def test_mock_policy_generates_actions(self):
+ policy = MockPolicy()
+ policy.set_robot_state_keys(JOINT_NAMES)
+ obs = {j: 0.0 for j in JOINT_NAMES}
+ actions = asyncio.run(policy.get_actions(obs, "test"))
+ assert len(actions) == 8
+ assert all(j in actions[0] for j in JOINT_NAMES)
+
+ def test_full_observe_act_loop(self, sim_env):
+ model, data = sim_env
+ policy = MockPolicy()
+ policy.set_robot_state_keys(JOINT_NAMES)
+
+ for step in range(20):
+ obs = read_joints(model, data)
+ actions = asyncio.run(policy.get_actions(obs, "pick up cube"))
+ apply_action(model, data, actions[0])
+ mj.mj_step(model, data)
+
+ assert data.time > 0
+ final_obs = read_joints(model, data)
+ # Joints should have moved from 0
+ assert any(abs(v) > 0.001 for v in final_obs.values())
+
+ @requires_gl
+ def test_loop_with_rendering(self, sim_env):
+ """Full loop: observe → policy → act → step → render (10 iterations)."""
+ model, data = sim_env
+ policy = MockPolicy()
+ policy.set_robot_state_keys(JOINT_NAMES)
+ renderer = mj.Renderer(model, height=120, width=160)
+
+ frames = []
+ for _ in range(10):
+ obs = read_joints(model, data)
+ actions = asyncio.run(policy.get_actions(obs, "wave"))
+ apply_action(model, data, actions[0])
+ mj.mj_step(model, data)
+
+ renderer.update_scene(data)
+ frames.append(renderer.render().copy())
+
+ assert len(frames) == 10
+ assert all(f.shape == (120, 160, 3) for f in frames)
+ # Frames should differ (robot is moving)
+ assert not np.array_equal(frames[0], frames[-1])
+ del renderer
+
+
+class TestDomainRandomization:
+ def test_color_randomization(self, sim_env):
+ model, data = sim_env
+ orig = model.geom_rgba.copy()
+ rng = np.random.default_rng(42)
+ for i in range(model.ngeom):
+ model.geom_rgba[i, :3] = rng.uniform(0.1, 1.0, size=3)
+ assert not np.array_equal(orig, model.geom_rgba)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-v"])
diff --git a/tests/test_physics.py b/tests/test_physics.py
new file mode 100644
index 0000000..17e03a2
--- /dev/null
+++ b/tests/test_physics.py
@@ -0,0 +1,350 @@
+"""Tests for PhysicsMixin — advanced MuJoCo physics features.
+
+Tests: raycasting, jacobians, energy, forces, state checkpointing,
+inverse dynamics, sensor readout, body introspection, runtime modification.
+
+Run: uv run pytest tests/test_physics.py -v
+"""
+
+import json
+import os
+
+import numpy as np
+import pytest
+
+mj = pytest.importorskip("mujoco")
+
+from strands_robots.simulation.mujoco.simulation import Simulation # noqa: E402
+
+ROBOT_XML = """
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+"""
+
+
+@pytest.fixture
+def sim():
+ """Create a Simulation with the test scene loaded directly."""
+ from strands_robots.simulation.models import SimStatus, SimWorld
+
+ s = Simulation(tool_name="test_sim", mesh=False)
+ s._world = SimWorld()
+ s._world._model = mj.MjModel.from_xml_string(ROBOT_XML)
+ s._world._data = mj.MjData(s._world._model)
+ s._world.status = SimStatus.IDLE
+ mj.mj_forward(s._world._model, s._world._data)
+ yield s
+ s.cleanup()
+
+
+class TestRaycasting:
+ def test_raycast_hits_ground(self, sim):
+ result = sim.raycast(origin=[0, 0, 2], direction=[0, 0, -1])
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ assert data["hit"] is True
+ assert data["distance"] is not None
+ assert data["distance"] > 0
+
+ def test_raycast_hits_box(self, sim):
+ result = sim.raycast(origin=[0, 0, 2], direction=[0, 0, -1])
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ assert data["hit"] is True
+ assert data["geom_name"] in ("box_geom", "ground")
+
+ def test_raycast_misses(self, sim):
+ result = sim.raycast(origin=[0, 0, 2], direction=[0, 0, 1]) # shooting up
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ assert data["hit"] is False
+
+ def test_multi_raycast(self, sim):
+ dirs = [[0, 0, -1], [1, 0, 0], [0, 1, 0], [0, 0, 1]]
+ result = sim.multi_raycast(origin=[0, 0, 2], directions=dirs)
+ assert result["status"] == "success"
+ rays = json.loads(result["content"][1]["text"])["rays"]
+ assert len(rays) == 4
+ # At least the downward ray should hit
+ assert rays[0]["distance"] is not None
+
+
+class TestJacobians:
+ def test_body_jacobian(self, sim):
+ result = sim.get_jacobian(body_name="link2")
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ assert len(data["jacp"]) == 3 # 3×nv
+ assert data["nv"] == sim._world._model.nv
+
+ def test_site_jacobian(self, sim):
+ result = sim.get_jacobian(site_name="end_effector")
+ assert result["status"] == "success"
+
+ def test_geom_jacobian(self, sim):
+ result = sim.get_jacobian(geom_name="link2_geom")
+ assert result["status"] == "success"
+
+ def test_jacobian_no_target(self, sim):
+ result = sim.get_jacobian()
+ assert result["status"] == "error"
+
+ def test_jacobian_invalid_body(self, sim):
+ result = sim.get_jacobian(body_name="nonexistent")
+ assert result["status"] == "error"
+
+
+class TestEnergy:
+ def test_get_energy(self, sim):
+ result = sim.get_energy()
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ assert "potential" in data
+ assert "kinetic" in data
+ assert "total" in data
+ # Box at height 0.5 should have nonzero potential energy
+ assert data["potential"] != 0 or data["kinetic"] != 0
+
+ def test_energy_changes_after_step(self, sim):
+ e1 = json.loads(sim.get_energy()["content"][1]["text"])
+ # Step physics to let box fall
+ for _ in range(100):
+ mj.mj_step(sim._world._model, sim._world._data)
+ e2 = json.loads(sim.get_energy()["content"][1]["text"])
+ # Kinetic energy should change (box falls)
+ assert e1["kinetic"] != e2["kinetic"] or e1["potential"] != e2["potential"]
+
+
+class TestExternalForces:
+ def test_apply_force(self, sim):
+ result = sim.apply_force(body_name="box1", force=[0, 0, 100])
+ assert result["status"] == "success"
+ assert "box1" in result["content"][0]["text"]
+
+ def test_apply_force_invalid_body(self, sim):
+ result = sim.apply_force(body_name="nonexistent", force=[0, 0, 10])
+ assert result["status"] == "error"
+
+ def test_force_changes_acceleration(self, sim):
+ # Get initial state
+ data = sim._world._data
+ old_qfrc = data.qfrc_applied.copy()
+ sim.apply_force(body_name="box1", force=[0, 0, 100])
+ # qfrc_applied should change
+ assert not np.array_equal(old_qfrc, data.qfrc_applied)
+
+
+class TestMassMatrix:
+ def test_get_mass_matrix(self, sim):
+ result = sim.get_mass_matrix()
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ nv = sim._world._model.nv
+ assert data["shape"] == [nv, nv]
+ assert data["rank"] > 0
+ assert data["total_mass"] > 0
+
+ def test_mass_diagonal_positive(self, sim):
+ result = sim.get_mass_matrix()
+ diag = json.loads(result["content"][1]["text"])["diagonal"]
+ assert all(d >= 0 for d in diag)
+
+
+class TestStateCheckpointing:
+ def test_save_and_load_state(self, sim):
+ # Set a known joint position
+ sim._world._data.qpos[7] = 1.0 # shoulder
+ mj.mj_forward(sim._world._model, sim._world._data)
+
+ # Save
+ result = sim.save_state(name="test_checkpoint")
+ assert result["status"] == "success"
+
+ # Change state
+ sim._world._data.qpos[7] = -1.0
+ mj.mj_forward(sim._world._model, sim._world._data)
+ assert sim._world._data.qpos[7] == pytest.approx(-1.0)
+
+ # Restore
+ result = sim.load_state(name="test_checkpoint")
+ assert result["status"] == "success"
+ assert sim._world._data.qpos[7] == pytest.approx(1.0)
+
+ def test_load_nonexistent_checkpoint(self, sim):
+ result = sim.load_state(name="doesnt_exist")
+ assert result["status"] == "error"
+
+
+class TestInverseDynamics:
+ def test_inverse_dynamics(self, sim):
+ mj.mj_forward(sim._world._model, sim._world._data)
+ result = sim.inverse_dynamics()
+ assert result["status"] == "success"
+ forces = json.loads(result["content"][1]["text"])["qfrc_inverse"]
+ assert "shoulder" in forces or "elbow" in forces
+
+
+class TestBodyState:
+ def test_get_body_state(self, sim):
+ result = sim.get_body_state(body_name="box1")
+ assert result["status"] == "success"
+ state = json.loads(result["content"][1]["text"])
+ assert "position" in state
+ assert "quaternion" in state
+ assert "linear_velocity" in state
+ assert "angular_velocity" in state
+ assert "mass" in state
+ assert len(state["position"]) == 3
+ assert len(state["quaternion"]) == 4
+ assert state["mass"] == pytest.approx(1.0)
+
+ def test_body_state_invalid(self, sim):
+ result = sim.get_body_state(body_name="nonexistent")
+ assert result["status"] == "error"
+
+
+class TestDirectJointControl:
+ def test_set_joint_positions(self, sim):
+ result = sim.set_joint_positions(positions={"shoulder": 0.5, "elbow": -0.3})
+ assert result["status"] == "success"
+ assert "2/2" in result["content"][0]["text"]
+
+ # Verify positions were set
+ model, data = sim._world._model, sim._world._data
+ shoulder_id = mj.mj_name2id(model, mj.mjtObj.mjOBJ_JOINT, "shoulder")
+ qpos_adr = model.jnt_qposadr[shoulder_id]
+ assert data.qpos[qpos_adr] == pytest.approx(0.5)
+
+ def test_set_joint_velocities(self, sim):
+ result = sim.set_joint_velocities(velocities={"shoulder": 1.0})
+ assert result["status"] == "success"
+
+
+class TestSensors:
+ def test_get_all_sensors(self, sim):
+ result = sim.get_sensor_data()
+ assert result["status"] == "success"
+ sensors = json.loads(result["content"][1]["text"])["sensors"]
+ assert "shoulder_pos" in sensors
+ assert "elbow_pos" in sensors
+
+ def test_get_specific_sensor(self, sim):
+ result = sim.get_sensor_data(sensor_name="shoulder_pos")
+ assert result["status"] == "success"
+ sensors = json.loads(result["content"][1]["text"])["sensors"]
+ assert len(sensors) == 1
+ assert "shoulder_pos" in sensors
+
+ def test_sensor_values_change(self, sim):
+ # Set shoulder position
+ sim.set_joint_positions(positions={"shoulder": 1.0})
+ result = sim.get_sensor_data(sensor_name="shoulder_pos")
+ val = json.loads(result["content"][1]["text"])["sensors"]["shoulder_pos"]["values"]
+ assert abs(val - 1.0) < 0.01
+
+
+class TestRuntimeModification:
+ def test_set_body_mass(self, sim):
+ result = sim.set_body_properties(body_name="box1", mass=5.0)
+ assert result["status"] == "success"
+ body_id = mj.mj_name2id(sim._world._model, mj.mjtObj.mjOBJ_BODY, "box1")
+ assert sim._world._model.body_mass[body_id] == pytest.approx(5.0)
+
+ def test_set_geom_color(self, sim):
+ result = sim.set_geom_properties(geom_name="box_geom", color=[0, 1, 0, 1])
+ assert result["status"] == "success"
+ geom_id = mj.mj_name2id(sim._world._model, mj.mjtObj.mjOBJ_GEOM, "box_geom")
+ assert sim._world._model.geom_rgba[geom_id][1] == pytest.approx(1.0)
+
+ def test_set_geom_friction(self, sim):
+ result = sim.set_geom_properties(geom_name="box_geom", friction=[0.5, 0.01, 0.001])
+ assert result["status"] == "success"
+
+ def test_invalid_geom(self, sim):
+ result = sim.set_geom_properties(geom_name="nonexistent", color=[1, 0, 0, 1])
+ assert result["status"] == "error"
+
+
+class TestContactForces:
+ def test_get_contact_forces_after_settling(self, sim):
+ # Let box fall and settle
+ for _ in range(500):
+ mj.mj_step(sim._world._model, sim._world._data)
+ result = sim.get_contact_forces()
+ assert result["status"] == "success"
+ # Box should be in contact with ground
+ contacts = json.loads(result["content"][1]["text"])["contacts"]
+ assert len(contacts) > 0
+ assert contacts[0]["normal_force"] != 0
+
+
+class TestForwardKinematics:
+ def test_forward_kinematics(self, sim):
+ result = sim.forward_kinematics()
+ assert result["status"] == "success"
+ bodies = json.loads(result["content"][1]["text"])["bodies"]
+ assert "box1" in bodies
+ assert "link1" in bodies
+ assert len(bodies["box1"]["position"]) == 3
+
+
+class TestTotalMass:
+ def test_get_total_mass(self, sim):
+ result = sim.get_total_mass()
+ assert result["status"] == "success"
+ data = json.loads(result["content"][1]["text"])
+ assert data["total_mass"] > 0
+ assert "box1" in data["bodies"]
+ assert data["bodies"]["box1"] == pytest.approx(1.0)
+
+
+class TestExportXML:
+ def test_export_xml_string(self, sim):
+ result = sim.export_xml()
+ assert result["status"] == "success"
+ text = result["content"][0]["text"]
+ assert "mujoco" in text.lower() or "Model XML" in text
+
+ def test_export_xml_file(self, sim, tmp_path):
+ path = str(tmp_path / "exported.xml")
+ result = sim.export_xml(output_path=path)
+ assert result["status"] == "success"
+ assert os.path.exists(path)
+ with open(path) as f:
+ content = f.read()
+ assert "= '3.14' and sys_platform == 'linux'",
+ "python_full_version < '3.14' and sys_platform == 'linux'",
+ "python_full_version >= '3.14' and sys_platform == 'win32'",
+ "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+ "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+ "python_full_version < '3.14' and sys_platform == 'win32'",
+ "python_full_version < '3.14' and sys_platform == 'emscripten'",
+ "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32'",
+]
+
+[[package]]
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+ { name = "torch" },
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.4"
+source = { registry = "https://pypi.org/simple" }
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