From a880b599702fb1601a9f9972f93f263c9daf81c8 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 15:12:27 -0600
Subject: [PATCH 01/11] code integrated for multi agent coordination

---
 .../best_model/actor_agent_robot_0.pt         | Bin 0 -> 105312 bytes
 .../best_model/actor_agent_robot_1.pt         | Bin 0 -> 105312 bytes
 .../best_model/critic_agent.pt                | Bin 0 -> 124121 bytes
 .../best_model/value_normalizer.pt            | Bin 0 -> 1379 bytes
 .../progress.txt                              |   0
 scripts/reinforcement_learning/harl/play.py   | 145 ++++
 .../harl/play_adversarial.py                  | 171 +++++
 scripts/reinforcement_learning/harl/train.py  | 134 ++++
 source/isaaclab_rl/setup.py                   |   3 +-
 .../multi_agent_coordination/__init__.py      |  38 +
 .../agents/__init__.py                        |   4 +
 .../agents/harl_happo_cfg.yaml                | 125 ++++
 .../anymal_c_multi_agent_bar_env.py           | 467 ++++++++++++
 .../h1_anymal_push_env.py                     | 689 ++++++++++++++++++
 14 files changed, 1775 insertions(+), 1 deletion(-)
 create mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_0.pt
 create mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_1.pt
 create mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/critic_agent.pt
 create mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/value_normalizer.pt
 create mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/progress.txt
 create mode 100644 scripts/reinforcement_learning/harl/play.py
 create mode 100644 scripts/reinforcement_learning/harl/play_adversarial.py
 create mode 100644 scripts/reinforcement_learning/harl/train.py
 create mode 100644 source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
 create mode 100644 source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
 create mode 100644 source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml
 create mode 100644 source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
 create mode 100644 source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py

diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_0.pt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_0.pt
new file mode 100644
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diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_1.pt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_1.pt
new file mode 100644
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zjK_`C9N=`o8X&+D@Z}d(JFNo>4p{??w*<Tz--)dQoLa2`0xbdk=b7d%6dbh%2(kq9
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HcmV?d00001

diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/value_normalizer.pt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/value_normalizer.pt
new file mode 100644
index 0000000000000000000000000000000000000000..df7dee58872a8e064783a26891af672f928fe556
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HcmV?d00001

diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/progress.txt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/progress.txt
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/scripts/reinforcement_learning/harl/play.py b/scripts/reinforcement_learning/harl/play.py
new file mode 100644
index 00000000000..fe8a31a0e6a
--- /dev/null
+++ b/scripts/reinforcement_learning/harl/play.py
@@ -0,0 +1,145 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Train an algorithm."""
+
+import argparse
+
+# import numpy as np
+import sys
+import torch
+
+from isaaclab.app import AppLauncher
+
+parser = argparse.ArgumentParser(description="Train an RL agent with HARL.")
+parser.add_argument(
+    "--algorithm",
+    type=str,
+    default="happo",
+    choices=[
+        "happo",
+        "hatrpo",
+        "haa2c",
+        "haddpg",
+        "hatd3",
+        "hasac",
+        "had3qn",
+        "maddpg",
+        "matd3",
+        "mappo",
+        "happo_adv",
+    ],
+    help="Algorithm name. Choose from: happo, hatrpo, haa2c, haddpg, hatd3, hasac, had3qn, maddpg, matd3, mappo.",
+)
+parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
+parser.add_argument("--task", type=str, default=None, help="Name of the task.")
+parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
+parser.add_argument("--num_env_steps", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument("--dir", type=str, default=None, help="folder with trained models")
+parser.add_argument("--debug", action="store_true", help="whether to run in debug mode for visualization")
+
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli, hydra_args = parser.parse_known_args()
+
+# clear out sys.argv for Hydra
+sys.argv = [sys.argv[0]] + hydra_args
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+from harl.runners import RUNNER_REGISTRY
+
+from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
+
+import isaaclab_tasks  # noqa: F401
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+algorithm = args_cli.algorithm.lower()
+agent_cfg_entry_point = f"harl_{algorithm}_cfg_entry_point"
+
+
+@hydra_task_config(args_cli.task, agent_cfg_entry_point)
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
+    args = args_cli.__dict__
+
+    args["env"] = "isaaclab"
+    args["algo"] = args["algorithm"]
+    args["exp_name"] = "play"
+
+    algo_args = agent_cfg
+
+    algo_args["eval"]["use_eval"] = False
+    algo_args["render"]["use_render"] = True
+    algo_args["train"]["model_dir"] = args["dir"]
+
+    env_args = {}
+    env_cfg.scene.num_envs = args["num_envs"]
+    env_args["task"] = args["task"]
+    env_args["config"] = env_cfg
+    env_args["video_settings"] = {}
+    env_args["video_settings"]["video"] = False
+    env_args["headless"] = args["headless"]
+    env_args["debug"] = args["debug"]
+
+    # create runner
+    runner = RUNNER_REGISTRY[args["algo"]](args, algo_args, env_args)
+
+    obs, _, _ = runner.env.reset()
+
+    max_action_space = 0
+
+    for agent_id, obs_space in runner.env.action_space.items():
+        if obs_space.shape[0] > max_action_space:
+            max_action_space = obs_space.shape[0]
+
+    actions = torch.zeros((args["num_envs"], runner.num_agents, max_action_space), dtype=torch.float32, device="cuda:0")
+    rnn_states = torch.zeros(
+        (
+            args["num_envs"],
+            runner.num_agents,
+            runner.recurrent_n,
+            runner.rnn_hidden_size,
+        ),
+        dtype=torch.float32,
+        device="cuda:0",
+    )
+    masks = torch.ones(
+        (args["num_envs"], runner.num_agents, 1),
+        dtype=torch.float32,
+    )
+
+    while simulation_app.is_running():
+        with torch.inference_mode():
+            for agent_id, (_, agent_obs) in enumerate(obs.items()):
+                action, _, rnn_state = runner.actor[agent_id].get_actions(
+                    agent_obs, rnn_states[:, agent_id, :], masks[:, agent_id, :], None, None
+                )
+                action_space = action.shape[1]
+                actions[:, agent_id, :action_space] = action
+                rnn_states[:, agent_id, :] = rnn_state
+
+            obs, _, _, dones, _, _ = runner.env.step(actions)
+
+            dones_env = torch.all(dones, dim=1)
+            masks = torch.ones((args["num_envs"], runner.num_agents, 1), dtype=torch.float32, device="cuda:0")
+            masks[dones_env] = 0.0
+            rnn_states[dones_env] = torch.zeros(
+                ((dones_env).sum(), runner.num_agents, runner.recurrent_n, runner.rnn_hidden_size),
+                dtype=torch.float32,
+                device="cuda:0",
+            )
+
+            if runner.env.unwrapped.sim._number_of_steps >= args["num_env_steps"]:
+                break
+
+    runner.env.close()
+
+
+if __name__ == "__main__":
+    main()
+    simulation_app.close()
diff --git a/scripts/reinforcement_learning/harl/play_adversarial.py b/scripts/reinforcement_learning/harl/play_adversarial.py
new file mode 100644
index 00000000000..387509bd302
--- /dev/null
+++ b/scripts/reinforcement_learning/harl/play_adversarial.py
@@ -0,0 +1,171 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Train an algorithm."""
+
+import argparse
+
+# import numpy as np
+import sys
+import torch
+from tqdm import tqdm
+from isaaclab.app import AppLauncher
+import pprint
+
+parser = argparse.ArgumentParser(description="Train an RL agent with HARL.")
+parser.add_argument(
+    "--algorithm",
+    type=str,
+    default="happo",
+    choices=[
+        "happo",
+        "hatrpo",
+        "haa2c",
+        "haddpg",
+        "hatd3",
+        "hasac",
+        "had3qn",
+        "maddpg",
+        "matd3",
+        "mappo",
+        "happo_adv",
+    ],
+    help="Algorithm name. Choose from: happo, hatrpo, haa2c, haddpg, hatd3, hasac, had3qn, maddpg, matd3, mappo.",
+)
+parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
+parser.add_argument("--task", type=str, default=None, help="Name of the task.")
+parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
+parser.add_argument("--num_env_steps", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument("--dir", type=str, default=None, help="folder with trained models")
+parser.add_argument("--debug", action="store_true", help="whether to run in debug mode for visualization")
+
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli, hydra_args = parser.parse_known_args()
+
+# clear out sys.argv for Hydra
+sys.argv = [sys.argv[0]] + hydra_args
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+from harl.runners import RUNNER_REGISTRY
+
+from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
+
+import isaaclab_tasks  # noqa: F401
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+algorithm = args_cli.algorithm.lower()
+agent_cfg_entry_point = f"harl_{algorithm}_cfg_entry_point"
+
+
+@hydra_task_config(args_cli.task, agent_cfg_entry_point)
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
+    args = args_cli.__dict__
+
+    args["env"] = "isaaclab"
+    args["algo"] = args["algorithm"]
+    args["exp_name"] = "play"
+
+    algo_args = agent_cfg
+
+    algo_args["eval"]["use_eval"] = False
+    algo_args["render"]["use_render"] = True
+    algo_args["train"]["model_dir"] = args["dir"]
+
+    env_args = {}
+    env_cfg.scene.num_envs = args["num_envs"]
+    env_args["task"] = args["task"]
+    env_args["config"] = env_cfg
+    env_args["video_settings"] = {}
+    env_args["video_settings"]["video"] = False
+    env_args["headless"] = args["headless"]
+    env_args["debug"] = True
+
+    # create runner
+    runner = RUNNER_REGISTRY[args["algo"]](args, algo_args, env_args)
+
+    obs, _, _ = runner.env.reset()
+
+    max_action_space = 0
+
+    for agent_id, obs_space in runner.env.action_space.items():
+        if type(obs_space) is dict:
+            for agent_id, agent_obs in obs_space.items():
+                if agent_obs.shape[0] > max_action_space:
+                    max_action_space = agent_obs.shape[0]
+        elif obs_space.shape[0] > max_action_space:
+            max_action_space = obs_space.shape[0]
+
+    actions = torch.zeros((args["num_envs"], runner.num_agents, max_action_space), dtype=torch.float32, device="cuda:0")
+    rnn_states = torch.zeros(
+        (
+            args["num_envs"],
+            runner.num_agents,
+            runner.recurrent_n,
+            runner.rnn_hidden_size,
+        ),
+        dtype=torch.float32,
+        device="cuda:0",
+    )
+    masks = torch.ones(
+        (args["num_envs"], runner.num_agents, 1),
+        dtype=torch.float32,
+    )
+    log_infos = {}
+    envs_completed = 0
+    pbar = tqdm(total=args["num_env_steps"], desc="Playing")
+    while simulation_app.is_running():
+        with torch.inference_mode():
+            for team, agents_obs in obs.items():
+                for agent_id, agent_obs in agents_obs.items():
+                    agent_num = runner.env.env._agent_map[agent_id]
+                    action, _, rnn_state = runner.actors[team][agent_id].get_actions(
+                        agent_obs, rnn_states[:, agent_num, :], masks[:, agent_num, :], None, None
+                    )
+                    action_space = action.shape[1]
+                    actions[:, agent_num, :action_space] = action
+                    rnn_states[:, agent_num, :] = rnn_state
+
+        # We don't want to randomize the reset buffer when evaluating
+        obs, _, _, dones, _, _ = runner.env.step(actions)
+        dones_env = torch.all(dones, dim=1)
+        curr_envs_completed = dones_env.sum().item()
+        envs_completed += curr_envs_completed
+
+        if curr_envs_completed > 0:
+            for k,v in runner.env.log_info.items():
+                if k not in log_infos:
+                    log_infos[k] = 0.0
+                log_infos[k] += (v * curr_envs_completed)
+
+        masks = torch.ones((args["num_envs"], runner.num_agents, 1), dtype=torch.float32, device="cuda:0")
+        masks[dones_env] = 0.0
+        rnn_states[dones_env] = torch.zeros(
+            ((dones_env).sum(), runner.num_agents, runner.recurrent_n, runner.rnn_hidden_size), # type: ignore
+            dtype=torch.float32,
+            device="cuda:0",
+        ) # type: ignore
+
+        num_steps = args["num_envs"] * runner.env.unwrapped.sim._number_of_steps 
+        pbar.update(num_steps - pbar.n)
+        if num_steps >= args["num_env_steps"]:
+            break
+        
+    runner.env.close()
+
+    for k in log_infos.keys():
+        log_infos[k] /= envs_completed if envs_completed > 0 else 1
+
+    pbar.close()
+    pprint.pprint(log_infos)
+
+
+if __name__ == "__main__":
+    main()
+    simulation_app.close()
diff --git a/scripts/reinforcement_learning/harl/train.py b/scripts/reinforcement_learning/harl/train.py
new file mode 100644
index 00000000000..8f7bf6b4afa
--- /dev/null
+++ b/scripts/reinforcement_learning/harl/train.py
@@ -0,0 +1,134 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""Train an algorithm."""
+
+import argparse
+import sys
+import time
+
+from isaaclab.app import AppLauncher
+
+parser = argparse.ArgumentParser(description="Train an RL agent with HARL.")
+parser.add_argument("--video", action="store_true", help="Record videos during training.")
+parser.add_argument("--video_length", type=int, default=500, help="Length of the recorded video (in steps).")
+parser.add_argument("--video_interval", type=int, default=20000, help="Interval between video recordings (in steps).")
+parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
+parser.add_argument("--task", type=str, default=None, help="Name of the task.")
+parser.add_argument("--seed", type=int, default=1, help="Seed used for the environment")
+parser.add_argument("--save_interval", type=int, default=None, help="How often to save the model")
+parser.add_argument("--save_checkpoints", action="store_true", default=False, help="Whether or not to save checkpoints")
+parser.add_argument("--checkpoint_interval", type=int, default=200, help="How often to save a model checkpoint")
+parser.add_argument("--log_interval", type=int, default=None, help="How often to log outputs")
+parser.add_argument("--exp_name", type=str, default="test", help="Name of the Experiment")
+parser.add_argument("--num_env_steps", type=int, default=None, help="RL Policy training iterations.")
+parser.add_argument("--dir", type=str, default=None, help="folder with trained models")
+parser.add_argument("--debug", action="store_true", help="whether to run in debug mode for visualization")
+parser.add_argument("--adversarial_training_mode", default="parallel", choices=["parallel", "ladder", "leapfrog"], help="the mode type for adversarial training,\
+                     note on ladder training with teams that are composed of heterogenous agents, the two teams must place the robots in the same order in their environment \
+                    for ladder to work")
+parser.add_argument("--adversarial_training_iterations", default=50_000_000, type=int,help="the number of iterations to swap training for adversarial modes like ladder and leapfrog")
+
+parser.add_argument(
+    "--algorithm",
+    type=str,
+    default="happo",
+    choices=[
+        "happo",
+        "hatrpo",
+        "haa2c",
+        "mappo",
+        "mappo_unshare",
+        "happo_adv"
+    ],
+    help="Algorithm name. Choose from: happo, hatrpo, haa2c, mappo, and mappo_unshare.",
+)
+
+# append AppLauncher cli args
+AppLauncher.add_app_launcher_args(parser)
+# parse the arguments
+args_cli, hydra_args = parser.parse_known_args()
+# always enable cameras to record video
+if args_cli.video:
+    args_cli.enable_cameras = True
+
+# clear out sys.argv for Hydra
+sys.argv = [sys.argv[0]] + hydra_args
+
+# launch omniverse app
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+import os
+
+from harl.runners import RUNNER_REGISTRY
+
+from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
+
+import isaaclab_tasks  # noqa: F401
+from isaaclab_tasks.utils.hydra import hydra_task_config
+
+algorithm = args_cli.algorithm.lower()
+agent_cfg_entry_point = f"harl_{algorithm}_cfg_entry_point"
+
+
+@hydra_task_config(args_cli.task, agent_cfg_entry_point)
+def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
+
+    args = args_cli.__dict__
+
+    args["env"] = "isaaclab"
+
+    args["algo"] = args["algorithm"]
+
+    algo_args = agent_cfg
+
+    algo_args["eval"]["use_eval"] = False
+    algo_args["train"]["n_rollout_threads"] = args["num_envs"]
+    algo_args["train"]["num_env_steps"] = args["num_env_steps"]
+    algo_args["train"]["eval_interval"] = args["save_interval"]
+    algo_args["train"]["save_checkpoints"] = args["save_checkpoints"]
+    algo_args["train"]["checkpoint_interval"] = args["checkpoint_interval"]
+    algo_args["train"]["log_interval"] = args["log_interval"]
+    algo_args["train"]["model_dir"] = args["dir"]
+    algo_args["seed"]["specify_seed"] = True
+    algo_args["seed"]["seed"] = args["seed"]
+    algo_args["algo"]["adversarial_training_mode"] = args["adversarial_training_mode"]
+    algo_args["algo"]["adversarial_training_iterations"] = args["adversarial_training_iterations"]
+
+    env_args = {}
+    env_cfg.scene.num_envs = args["num_envs"]
+    env_args["task"] = args["task"]
+    env_args["config"] = env_cfg
+    hms_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+    env_args["video_settings"] = {
+        "video": True if args["video"] else False,
+        "video_length": args["video_length"],
+        "video_interval": args["video_interval"],
+        "log_dir": os.path.join(
+            algo_args["logger"]["log_dir"],
+            "isaaclab",
+            args["task"],
+            args["algorithm"],
+            args["exp_name"],
+            "-".join(["seed-{:0>5}".format(agent_cfg["seed"]["seed"]), hms_time]),
+            "videos",
+        ),
+    }
+
+    env_args["headless"] = args["headless"]
+    env_args["debug"] = args["debug"]
+
+
+    # create runner
+
+    runner = RUNNER_REGISTRY[args["algo"]](args, algo_args, env_args)
+    runner.run()
+    runner.close()
+
+
+if __name__ == "__main__":
+    main() # type: ignore
+    simulation_app.close()
diff --git a/source/isaaclab_rl/setup.py b/source/isaaclab_rl/setup.py
index 173c8257c73..3c1b2b58b2a 100644
--- a/source/isaaclab_rl/setup.py
+++ b/source/isaaclab_rl/setup.py
@@ -46,7 +46,8 @@
         "rl-games @ git+https://github.com/isaac-sim/rl_games.git@python3.11",
         "gym",
     ],  # rl-games still needs gym :(
-    "rsl-rl": ["rsl-rl-lib==3.0.1", "onnxscript>=0.5"],  # linux aarch 64 requires manual onnxscript installation
+    "rsl-rl": ["rsl-rl-lib==3.0.1", "onnxscript>=0.5"],  # linux aarch 64 requires manual onnxscript installation,
+    "harl": ["harl@git+https://github.com/DIRECTLab/HARL.git"],
 }
 # Add the names with hyphens as aliases for convenience
 EXTRAS_REQUIRE["rl_games"] = EXTRAS_REQUIRE["rl-games"]
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
new file mode 100644
index 00000000000..bfd24f9a1d3
--- /dev/null
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+"""
+Ant locomotion environment.
+"""
+
+import gymnasium as gym
+
+from . import agents
+from .anymal_c_multi_agent_bar_env import AnymalCMultiAgentBar, AnymalCMultiAgentBarEnvCfg
+from .h1_anymal_push_env import HeterogeneousPushMultiAgentEnv, HeterogeneousPushMultiAgentEnvCfg
+
+##
+# Register Gym environments.
+##
+
+gym.register(
+    id="Isaac-Multi-Agent-Anymal-C-Bar-Direct-v0",
+    entry_point=AnymalCMultiAgentBar,
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": AnymalCMultiAgentBarEnvCfg,
+        "harl_happo_cfg_entry_point": f"{agents.__name__}:harl_happo_cfg.yaml",
+    },
+)
+
+gym.register(
+    id="Isaac-Multi-Agent-H1-Anymal-C-Push-Direct-v0",
+    entry_point=HeterogeneousPushMultiAgentEnv,
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": HeterogeneousPushMultiAgentEnvCfg,
+        "harl_happo_cfg_entry_point": f"{agents.__name__}:harl_happo_cfg.yaml",
+    },
+)
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
new file mode 100644
index 00000000000..e75ca2bc3f9
--- /dev/null
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml
new file mode 100644
index 00000000000..89208957ac9
--- /dev/null
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml
@@ -0,0 +1,125 @@
+# This is the configuration file for the HAPPO algorithm.
+seed:
+  # whether to use the specified seed
+  seed_specify: True
+  # seed
+  seed: 1
+device:
+  # whether to use CUDA
+  cuda: True
+  # whether to set CUDA deterministic
+  cuda_deterministic: True
+  # arg to torch.set_num_threads
+  torch_threads: 4
+  # if you have multiple GPUs, you can specify which GPU to use
+  gpu_id: 0
+train:
+  # number of parallel environments for training data collection
+  n_rollout_threads: 20
+  # number of total training steps
+  num_env_steps: 10000000
+  # number of steps per environment per training data collection
+  episode_length: 200
+  # logging interval, also the interval by which the average done reward is checked
+  log_interval: 5
+  # evaluation interval
+  eval_interval: 25
+  # whether to use ValueNorm
+  use_valuenorm: True
+  # whether to use linear learning rate decay
+  use_linear_lr_decay: False
+  # whether to consider the case of truncation when an episode is done
+  use_proper_time_limits: True
+  # if set, load models from this directory; otherwise, randomly initialise the models
+  model_dir: #
+  # whether or not to save the entire model, or just the state dict
+  save_entire_model: False
+eval:
+  # whether to use evaluation
+  use_eval: True
+  # number of parallel environments for evaluation
+  n_eval_rollout_threads: 10
+  # number of episodes per evaluation
+  eval_episodes: 20
+render:
+  # whether to use render
+  use_render: False
+  # number of episodes to render
+  render_episodes: 10
+model:
+  # network parameters
+  # hidden sizes for mlp module in the network
+  hidden_sizes: [128, 128]
+  # activation function, choose from sigmoid, tanh, relu, leaky_relu, selu
+  activation_func: relu
+  # whether to use feature normalization
+  use_feature_normalization: True
+  # initialization method for network parameters, choose from xavier_uniform_, orthogonal_, ...
+  initialization_method: orthogonal_
+  # gain of the output layer of the network.
+  gain: 0.01
+  # recurrent parameters
+  # whether to use rnn policy (data is not chunked for training)
+  use_naive_recurrent_policy: False
+  # whether to use rnn policy (data is chunked for training)
+  use_recurrent_policy: False
+  # number of recurrent layers
+  recurrent_n: 1
+  # length of data chunk; only useful when use_recurrent_policy is True; episode_length has to be a multiple of data_chunk_length
+  data_chunk_length: 10
+  # optimizer parameters
+  # actor learning rate
+  lr: 0.0005
+  # critic learning rate
+  critic_lr: 0.0005
+  # eps in Adam
+  opti_eps: 0.00001
+  # weight_decay in Adam
+  weight_decay: 0
+  # parameters of diagonal Gaussian distribution
+  std_x_coef: 1
+  # parameters of diagonal Gaussian distribution
+  std_y_coef: 0.5
+algo:
+  # ppo parameters
+  # number of epochs for actor update
+  ppo_epoch: 5
+  # number of epochs for critic update
+  critic_epoch: 5
+  # whether to use clipped value loss
+  use_clipped_value_loss: True
+  # clip parameter
+  clip_param: 0.2
+  # number of mini-batches per epoch for actor update
+  actor_num_mini_batch: 1
+  # number of mini-batches per epoch for critic update
+  critic_num_mini_batch: 1
+  # coefficient for entropy term in actor loss
+  entropy_coef: 0.01
+  # coefficient for value loss
+  value_loss_coef: 1
+  # whether to clip gradient norm
+  use_max_grad_norm: True
+  # max gradient norm
+  max_grad_norm: 10.0
+  # whether to use Generalized Advantage Estimation (GAE)
+  use_gae: True
+  # discount factor
+  gamma: 0.99
+  # GAE lambda
+  gae_lambda: 0.95
+  # whether to use huber loss
+  use_huber_loss: True
+  # whether to use policy active masks
+  use_policy_active_masks: True
+  # huber delta
+  huber_delta: 10.0
+  # method of aggregating the probability of multi-dimensional actions, choose from prod, mean
+  action_aggregation: prod
+  # whether to share parameter among actors
+  share_param: False
+  # whether to use a fixed optimisation order
+  fixed_order: False
+logger:
+  # logging directory
+  log_dir: "./results"
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
new file mode 100644
index 00000000000..46060ecad65
--- /dev/null
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
@@ -0,0 +1,467 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import copy
+import torch
+
+import isaaclab.envs.mdp as mdp
+import isaaclab.sim as sim_utils
+from isaaclab.assets import Articulation, ArticulationCfg, RigidObject, RigidObjectCfg
+from isaaclab.envs import DirectMARLEnv, DirectMARLEnvCfg
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.sensors import ContactSensor, ContactSensorCfg
+from isaaclab.sim import SimulationCfg
+from isaaclab.terrains import TerrainImporterCfg
+from isaaclab.utils import configclass
+from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
+from isaaclab.utils.math import quat_from_angle_axis
+
+##
+# Pre-defined configs
+##
+from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
+from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG  # isort: skip
+
+
+@configclass
+class EventCfg:
+    """Configuration for randomization."""
+
+    physics_material_0 = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_0", body_names=".*"),
+            "static_friction_range": (0.8, 0.8),
+            "dynamic_friction_range": (0.6, 0.6),
+            "restitution_range": (0.0, 0.0),
+            "num_buckets": 64,
+        },
+    )
+
+    physics_material_1 = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_1", body_names=".*"),
+            "static_friction_range": (0.8, 0.8),
+            "dynamic_friction_range": (0.6, 0.6),
+            "restitution_range": (0.0, 0.0),
+            "num_buckets": 64,
+        },
+    )
+
+    add_base_mass_0 = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_0", body_names="base"),
+            "mass_distribution_params": (-5.0, 5.0),
+            "operation": "add",
+        },
+    )
+
+    add_base_mass_1 = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_1", body_names="base"),
+            "mass_distribution_params": (-5.0, 5.0),
+            "operation": "add",
+        },
+    )
+
+
+def define_markers() -> VisualizationMarkers:
+    marker_cfg = VisualizationMarkersCfg(
+        prim_path="/Visuals/myMarkers",
+        markers={
+            "sphere1": sim_utils.SphereCfg(
+                radius=0.1,
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.0)),
+            ),
+            "sphere2": sim_utils.SphereCfg(
+                radius=0.1,
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)),
+            ),
+            "arrow1": sim_utils.UsdFileCfg(
+                usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd",
+                scale=(0.1, 0.1, 1.0),
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.0)),
+            ),
+            "arrow2": sim_utils.UsdFileCfg(
+                usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd",
+                scale=(0.1, 0.1, 1.0),
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)),
+            ),
+        },
+    )
+    return VisualizationMarkers(marker_cfg)
+
+
+@configclass
+class AnymalCMultiAgentBarEnvCfg(DirectMARLEnvCfg):
+    # env
+    episode_length_s = 20.0
+    decimation = 4
+    action_scale = 0.5
+    action_space = 12
+    action_spaces = {f"robot_{i}": 12 for i in range(2)}
+    # observation_space = 48
+    observation_space = 48
+    observation_spaces = {f"robot_{i}": 48 for i in range(2)}
+    state_space = 0
+    state_spaces = {f"robot_{i}": 0 for i in range(2)}
+    possible_agents = ["robot_0", "robot_1"]
+
+    # simulation
+    sim: SimulationCfg = SimulationCfg(
+        dt=1 / 200,
+        render_interval=decimation,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=0.0,
+        ),
+    )
+    terrain = TerrainImporterCfg(
+        prim_path="/World/ground",
+        terrain_type="plane",
+        collision_group=-1,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=0.0,
+        ),
+        debug_vis=False,
+    )
+
+    # scene
+    scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=6.0, replicate_physics=True)
+
+    # events
+    events: EventCfg = EventCfg()
+
+    # robot
+    robot_0: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot_0")
+    contact_sensor_0: ContactSensorCfg = ContactSensorCfg(
+        prim_path="/World/envs/env_.*/Robot_0/.*", history_length=3, update_period=0.005, track_air_time=True
+    )
+    robot_0.init_state.rot = (1.0, 0.0, 0.0, 1)
+    robot_0.init_state.pos = (-1.0, 0.0, 0.5)
+
+    robot_1: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot_1")
+    contact_sensor_1: ContactSensorCfg = ContactSensorCfg(
+        prim_path="/World/envs/env_.*/Robot_1/.*", history_length=3, update_period=0.005, track_air_time=True
+    )
+    robot_1.init_state.rot = (1.0, 0.0, 0.0, 1)
+    robot_1.init_state.pos = (1.0, 0.0, 0.5)
+
+    # rec prism
+    cfg_rec_prism = RigidObjectCfg(
+        prim_path="/World/envs/env_.*/Object",
+        spawn=sim_utils.CuboidCfg(
+            size=(5, 0.1, 0.1),
+            rigid_props=sim_utils.RigidBodyPropertiesCfg(),
+            mass_props=sim_utils.MassPropertiesCfg(mass=0.01),  # changed from 1.0 to 0.5
+            collision_props=sim_utils.CollisionPropertiesCfg(),
+            visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)),
+        ),
+        init_state=RigidObjectCfg.InitialStateCfg(
+            pos=(0.0, 0, 0.61), rot=(1.0, 0.0, 0.0, 0.0)
+        ),  # started the bar lower
+    )
+
+    # reward scales
+    lin_vel_reward_scale = 1.0
+    yaw_rate_reward_scale = 1.0
+
+    bar_z_min_pos = .6
+
+    anymal_min_z_pos = 0.1
+    max_bar_roll_angle_rad = 1
+
+
+class AnymalCMultiAgentBar(DirectMARLEnv):
+    cfg: AnymalCMultiAgentBarEnvCfg
+
+    def __init__(
+        self, cfg: AnymalCMultiAgentBarEnvCfg, render_mode: str | None = None, **kwargs
+    ):
+        super().__init__(cfg, render_mode, **kwargs)
+        # Joint position command (deviation from default joint positions)
+
+        self.actions = {
+            agent: torch.zeros(self.num_envs, action_space, device=self.device)
+            for agent, action_space in self.cfg.action_spaces.items()
+        }
+        self.previous_actions = {
+            agent: torch.zeros(self.num_envs, action_space, device=self.device)
+            for agent, action_space in self.cfg.action_spaces.items()
+        }
+        # X/Y linear velocity and yaw angular velocity commands
+        self._commands = torch.zeros(self.num_envs, 3, device=self.device)
+
+        # Logging
+        self._episode_sums = {
+            key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
+            for key in [
+                "track_lin_vel_xy_exp",
+                "track_ang_vel_z_exp",
+            ]
+        }
+
+        self.base_ids = {}
+        self.feet_ids = {}
+        self.undesired_body_contact_ids = {}
+
+        for robot_id, contact_sensor in self.contact_sensors.items():
+            _base_id, _ = contact_sensor.find_bodies("base")
+            _feet_ids, _ = contact_sensor.find_bodies(".*FOOT")
+            _undesired_contact_body_ids, _ = contact_sensor.find_bodies(".*THIGH")
+            self.base_ids[robot_id] = _base_id
+            self.feet_ids[robot_id] = _feet_ids
+            self.undesired_body_contact_ids[robot_id] = _undesired_contact_body_ids
+
+    def _setup_scene(self):
+        self.num_robots = sum(1 for key in self.cfg.__dict__.keys() if "robot_" in key)
+        self.robots = {}
+        self.contact_sensors = {}
+        self.height_scanners = {}
+        self.object = RigidObject(self.cfg.cfg_rec_prism)
+        self.my_visualizer = define_markers()
+        self.scene.rigid_objects["object"] = self.object
+
+        for i in range(self.num_robots):
+            self.robots[f"robot_{i}"] = Articulation(self.cfg.__dict__["robot_" + str(i)])
+            self.scene.articulations[f"robot_{i}"] = self.robots[f"robot_{i}"]
+            self.contact_sensors[f"robot_{i}"] = ContactSensor(self.cfg.__dict__["contact_sensor_" + str(i)])
+            self.scene.sensors[f"robot_{i}"] = self.contact_sensors[f"robot_{i}"]
+
+        self.cfg.terrain.num_envs = self.scene.cfg.num_envs
+        self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing
+        self._terrain = self.cfg.terrain.class_type(self.cfg.terrain)
+        # clone, filter, and replicate
+        self.scene.clone_environments(copy_from_source=False)
+        self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path])
+        # add lights
+        light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75))
+        light_cfg.func("/World/Light", light_cfg)
+
+    def _pre_physics_step(self, actions: torch.Tensor):
+        # We need to process the actions for each scene independently
+        self.processed_actions = copy.deepcopy(actions)
+        for robot_id, robot in self.robots.items():
+            self.actions[robot_id] = actions[robot_id].clone()
+            self.processed_actions[robot_id] = (
+                self.cfg.action_scale * self.actions[robot_id] + robot.data.default_joint_pos
+            )
+
+    def _apply_action(self):
+        for robot_id, robot in self.robots.items():
+            robot.set_joint_position_target(self.processed_actions[robot_id])
+
+    def _get_observations(self) -> dict:
+        self.previous_actions = copy.deepcopy(self.actions)
+
+        obs = {}
+
+        for robot_id, robot in self.robots.items():
+            obs[robot_id] = torch.cat(
+                [
+                    tensor
+                    for tensor in (
+                        robot.data.root_com_lin_vel_b,
+                        robot.data.root_com_ang_vel_b,
+                        robot.data.projected_gravity_b,
+                        self._commands,
+                        robot.data.joint_pos - robot.data.default_joint_pos,
+                        robot.data.joint_vel,
+                        self.actions[robot_id],
+                    )
+                    if tensor is not None
+                ],
+                dim=-1,
+            )
+        # obs = torch.cat(obs, dim=0)
+        # observations = {"policy": obs}
+        return obs
+
+    def get_y_euler_from_quat(self, quaternion):
+        w, x, y, z = quaternion[:, 0], quaternion[:, 1], quaternion[:, 2], quaternion[:, 3]
+        y_euler_angle = torch.arcsin(2 * (w * y - z * x))
+        return y_euler_angle
+
+    def _draw_markers(self, command):
+        xy_commands = command.clone()
+        z_commands = xy_commands[:, 2].clone()
+        xy_commands[:, 2] = 0
+
+        marker_ids = torch.concat(
+            [
+                0 * torch.zeros(2 * self._commands.shape[0]),
+                1 * torch.ones(self._commands.shape[0]),
+                2 * torch.ones(self._commands.shape[0]),
+                3 * torch.ones(self._commands.shape[0]),
+            ],
+            dim=0,
+        )
+
+        bar_pos = self.object.data.body_com_pos_w.squeeze(1).clone()
+        bar_yaw = self.object.data.root_com_ang_vel_b[:, 2].clone()
+
+        scale1 = torch.ones((self._commands.shape[0], 3), device=self.device)
+        scale1[:, 0] = torch.abs(z_commands)
+
+        scale2 = torch.ones((self._commands.shape[0], 3), device=self.device)
+        scale2[:, 0] = torch.abs(bar_yaw)
+
+        offset1 = torch.zeros((self._commands.shape[0], 3), device=self.device)
+        offset1[:, 1] = 0
+
+        offset2 = torch.zeros((self._commands.shape[0], 3), device=self.device)
+        offset2[:, 1] = 0
+
+        _90 = (-3.14 / 2) * torch.ones(self._commands.shape[0]).to(self.device)
+
+        marker_orientations = quat_from_angle_axis(
+            torch.concat(
+                [
+                    torch.zeros(3 * self._commands.shape[0]).to(self.device),
+                    torch.sign(z_commands) * _90,
+                    torch.sign(bar_yaw) * _90,
+                ],
+                dim=0,
+            ),
+            torch.tensor([0.0, 1.0, 0.0], device=self.device),
+        )
+
+        marker_scales = torch.concat(
+            [torch.ones((3 * self._commands.shape[0], 3), device=self.device), scale1, scale2], dim=0
+        )
+
+        marker_locations = torch.concat(
+            [
+                bar_pos,
+                bar_pos + xy_commands,
+                bar_pos + self.object.data.root_com_lin_vel_b,
+                bar_pos + offset1,
+                bar_pos + offset2,
+            ],
+            dim=0,
+        )
+
+        self.my_visualizer.visualize(
+            marker_locations, marker_orientations, scales=marker_scales, marker_indices=marker_ids
+        )
+
+    def _get_rewards(self) -> dict:
+        bar_commands = torch.stack([-self._commands[:, 1], self._commands[:, 0], self._commands[:, 2]]).t()
+
+        self._draw_markers(bar_commands)
+
+        # xy linear velocity tracking
+        lin_vel_error = torch.sum(
+            torch.square(bar_commands[:, :2] - self.object.data.root_com_lin_vel_b[:, :2]), dim=1
+        )
+        lin_vel_error_mapped = torch.exp(-lin_vel_error)
+
+        # yaw rate tracking
+        yaw_rate_error = torch.square(self._commands[:, 2] - self.object.data.root_com_ang_vel_b[:, 2])
+        yaw_rate_error_mapped = torch.exp(-yaw_rate_error)
+
+        rewards = {
+            "track_lin_vel_xy_exp": lin_vel_error_mapped * self.cfg.lin_vel_reward_scale * self.step_dt,
+            "track_ang_vel_z_exp": yaw_rate_error_mapped * self.cfg.yaw_rate_reward_scale * self.step_dt,
+        }
+        reward = torch.sum(torch.stack(list(rewards.values())), dim=0)
+
+        # Logging
+        for key, value in rewards.items():
+            self._episode_sums[key] += value
+
+        return {"robot_0":reward, "robot_1":reward}
+
+    def _get_anymal_fallen(self):
+        agent_dones = []
+
+        for _, robot in self.robots.items():
+            died = robot.data.body_com_pos_w[:, 0, 2].view(-1) < self.cfg.anymal_min_z_pos
+            agent_dones.append(died)
+
+        return torch.any(torch.stack(agent_dones), dim=0)
+
+    def _get_bar_fallen(self):
+        bar_z_pos = self.object.data.body_com_pos_w[:, :, 2].view(-1)
+        bar_roll_angle = torch.abs(self.get_y_euler_from_quat(self.object.data.root_com_quat_w))
+
+        bar_angle_maxes = bar_roll_angle > self.cfg.max_bar_roll_angle_rad
+        bar_fallen = bar_z_pos < self.cfg.bar_z_min_pos
+
+        return torch.logical_or(bar_angle_maxes, bar_fallen)
+
+    def _get_timeouts(self):
+        return self.episode_length_buf >= self.max_episode_length - 1
+
+    def _get_dones(self) -> tuple[dict, dict]:
+        time_out = self._get_timeouts()
+        anymal_fallen = self._get_anymal_fallen()
+        bar_fallen = self._get_bar_fallen()
+
+        dones = torch.logical_or(anymal_fallen, bar_fallen)
+
+        return {key: time_out for key in self.robots.keys()}, {key: dones for key in self.robots.keys()}
+
+    def _reset_idx(self, env_ids: torch.Tensor):
+        super()._reset_idx(env_ids)
+
+        object_default_state = self.object.data.default_root_state.clone()[env_ids]
+        object_default_state[:, 0:3] = object_default_state[:, 0:3] + self.scene.env_origins[env_ids]
+        self.object.write_root_state_to_sim(object_default_state, env_ids)
+        self.object.reset(env_ids)
+
+        for agent, _ in self.cfg.action_spaces.items():
+            self.actions[agent][env_ids] = 0.0
+            self.previous_actions[agent][env_ids] = 0.0
+
+        self._commands[env_ids] = torch.zeros_like(self._commands[env_ids]).uniform_(-1.0, 1.0)
+
+        for _, robot in self.robots.items():
+            if env_ids is None or len(env_ids) == self.num_envs:
+                env_ids = robot._ALL_INDICES
+            robot.reset(env_ids)
+            if len(env_ids) == self.num_envs:
+                # Spread out the resets to avoid spikes in training when many environments reset at a similar time
+                self.episode_length_buf[:] = torch.randint_like(
+                    self.episode_length_buf, high=int(self.max_episode_length)
+                )
+
+            # Reset robot state
+            joint_pos = robot.data.default_joint_pos[env_ids]
+            joint_vel = robot.data.default_joint_vel[env_ids]
+            default_root_state = robot.data.default_root_state[env_ids]
+            default_root_state[:, :3] += self._terrain.env_origins[env_ids]
+            robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids)
+            robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids)
+            robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids)
+        # Logging
+        extras = dict()
+        for key in self._episode_sums.keys():
+            episodic_sum_avg = torch.mean(self._episode_sums[key][env_ids])
+            extras["Episode_Reward_s/" + key] = episodic_sum_avg / self.max_episode_length_s
+            self._episode_sums[key][env_ids] = 0.0
+        self.extras["log"] = dict()
+        self.extras["log"].update(extras)
+
+
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
new file mode 100644
index 00000000000..06d1e4a4582
--- /dev/null
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
@@ -0,0 +1,689 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import copy
+import torch
+
+import isaacsim.core.utils.torch as torch_utils
+from isaacsim.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
+
+import isaaclab.envs.mdp as mdp
+import isaaclab.sim as sim_utils
+from isaaclab.assets import Articulation, ArticulationCfg, RigidObject, RigidObjectCfg
+from isaaclab.envs import DirectMARLEnv, DirectMARLEnvCfg
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.sensors import ContactSensor, ContactSensorCfg, RayCasterCfg, patterns
+from isaaclab.sim import SimulationCfg
+from isaaclab.terrains import TerrainImporterCfg
+from isaaclab.utils import configclass
+from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
+from isaaclab.utils.math import quat_from_angle_axis
+
+##
+# Pre-defined configs
+##
+from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
+from isaaclab_assets.robots.unitree import H1_CFG  # isort: skip
+from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG  # isort: skip
+
+
+def normalize_angle(x):
+    return torch.atan2(torch.sin(x), torch.cos(x))
+
+
+@configclass
+class EventCfg:
+    """Configuration for randomization."""
+
+    physics_material_0 = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_0", body_names=".*"),
+            "static_friction_range": (0.8, 0.8),
+            "dynamic_friction_range": (0.6, 0.6),
+            "restitution_range": (0.0, 0.0),
+            "num_buckets": 64,
+        },
+    )
+
+    physics_material_1 = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_1", body_names=".*"),
+            "static_friction_range": (0.8, 0.8),
+            "dynamic_friction_range": (0.6, 0.6),
+            "restitution_range": (0.0, 0.0),
+            "num_buckets": 64,
+        },
+    )
+
+    add_base_mass_0 = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_0", body_names="base"),
+            "mass_distribution_params": (-5.0, 5.0),
+            "operation": "add",
+        },
+    )
+
+    add_base_mass_1 = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_1", body_names="pelvis"),
+            "mass_distribution_params": (-5.0, 5.0),
+            "operation": "add",
+        },
+    )
+
+
+@configclass
+class HeterogeneousPushMultiAgentEnvCfg(DirectMARLEnvCfg):
+    # env
+    episode_length_s = 20.0
+    decimation = 4
+    anymal_action_scale = 0.5
+    action_space = 12
+    action_spaces = {"robot_0": 12, "robot_1": 19}
+
+    observation_spaces = {"robot_0": 48, "robot_1": 72}
+    state_space = 0
+    state_spaces = {f"robot_{i}": 0 for i in range(2)}
+    possible_agents = ["robot_0", "robot_1"]
+
+    # simulation
+    sim: SimulationCfg = SimulationCfg(
+        dt=1 / 200,
+        render_interval=decimation,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=0.0,
+        ),
+    )
+    terrain = TerrainImporterCfg(
+        prim_path="/World/ground",
+        terrain_type="plane",
+        collision_group=-1,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=0.0,
+        ),
+        debug_vis=False,
+    )
+
+    # scene
+    scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=6.0, replicate_physics=True)
+
+    # events
+    events: EventCfg = EventCfg()
+
+    # robot
+    robot_0: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot_0")
+    contact_sensor_0: ContactSensorCfg = ContactSensorCfg(
+        prim_path="/World/envs/env_.*/Robot_0/.*", history_length=3, update_period=0.005, track_air_time=True
+    )
+    robot_0.init_state.rot = (1.0, 0.0, 0.0, 1.0)
+    robot_0.init_state.pos = (-1.0, 0.0, 0.5)
+
+    robot_1: ArticulationCfg = H1_CFG.replace(prim_path="/World/envs/env_.*/Robot_1")
+    robot_1.init_state.rot = (1.0, 0.0, 0.0, 1.0)
+    robot_1.init_state.pos = (1.0, 0.0, 1.0)
+
+    # rec prism
+    cfg_rec_prism = RigidObjectCfg(
+        prim_path="/World/envs/env_.*/Object",
+        spawn=sim_utils.CuboidCfg(
+            size=(3, 2, 2),
+            rigid_props=sim_utils.RigidBodyPropertiesCfg(),
+            mass_props=sim_utils.MassPropertiesCfg(mass=10),
+            collision_props=sim_utils.CollisionPropertiesCfg(),
+            visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)),
+            # visual_material=sim_utils.GlassMdlCfg(glass_color=(0.0, 1.0, 0.0), frosting_roughness=0.7),
+        ),
+        init_state=RigidObjectCfg.InitialStateCfg(pos=(0.0, 2, 0.6), rot=(1.0, 0.0, 0.0, 0.0)),  # started the bar lower
+    )
+
+    # reward scales (override from flat config)
+    flat_orientation_reward_scale = 0.0
+
+    # reward scales
+    lin_vel_reward_scale = 1.0
+    yaw_rate_reward_scale = 0.5
+    z_vel_reward_scale = -2.0
+    ang_vel_reward_scale = -0.05
+    joint_torque_reward_scale = -2.5e-5
+    joint_accel_reward_scale = -2.5e-7
+    action_rate_reward_scale = -0.01
+    feet_air_time_reward_scale = 0.5
+    undersired_contact_reward_scale = -1.0
+    flat_orientation_reward_scale = -5.0
+    flat_bar_roll_angle_reward_scale = -1.0
+    angular_velocity_scale: float = 0.25
+    dof_vel_scale: float = 0.1
+    h1_action_scale = 1.0
+    termination_height: float = 0.8
+    anymal_min_z_pos = 0.3
+    h1_min_z_pos = 0.8
+
+    joint_gears: list = [
+        50.0,  # left_hip_yaw
+        50.0,  # right_hip_yaw
+        50.0,  # torso
+        50.0,  # left_hip_roll
+        50.0,  # right_hip_roll
+        50.0,  # left_shoulder_pitch
+        50.0,  # right_shoulder_pitch
+        50.0,  # left_hip_pitch
+        50.0,  # right_hip_pitch
+        50.0,  # left_shoulder_roll
+        50.0,  # right_shoulder_roll
+        50.0,  # left_knee
+        50.0,  # right_knee
+        50.0,  # left_shoulder_yaw
+        50.0,  # right_shoulder_yaw
+        50.0,  # left_ankle
+        50.0,  # right_ankle
+        50.0,  # left_elbow
+        50.0,  # right_elbow
+    ]
+
+def define_markers() -> VisualizationMarkers:
+    marker_cfg = VisualizationMarkersCfg(
+        prim_path="/Visuals/myMarkers",
+        markers={
+            "sphere1": sim_utils.SphereCfg(
+                radius=0.15,
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.0)),
+            ),
+            "sphere2": sim_utils.SphereCfg(
+                radius=0.15,
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)),
+            ),
+            "arrow1": sim_utils.UsdFileCfg(
+                usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd",
+                scale=(0.1, 0.1, 1.0),
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.0)),
+            ),
+            "arrow2": sim_utils.UsdFileCfg(
+                usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd",
+                scale=(0.1, 0.1, 1.0),
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)),
+            ),
+        },
+    )
+    return VisualizationMarkers(marker_cfg)
+
+class HeterogeneousPushMultiAgentEnv(DirectMARLEnv):
+    cfg: HeterogeneousPushMultiAgentEnvCfg
+
+    def __init__(
+        self,
+        cfg: HeterogeneousPushMultiAgentEnvCfg,
+        render_mode: str | None = None,
+        **kwargs,
+    ):
+        super().__init__(cfg, render_mode, **kwargs)
+        # Joint position command (deviation from default joint positions)
+        self.actions = {
+            agent: torch.zeros(self.num_envs, action_space, device=self.device)
+            for agent, action_space in self.cfg.action_spaces.items()
+        }
+        self.previous_actions = {
+            agent: torch.zeros(self.num_envs, action_space, device=self.device)
+            for agent, action_space in self.cfg.action_spaces.items()
+        }
+        self.base_bodies = ["base", "pelvis"]
+        # X/Y linear velocity and yaw angular velocity commands
+        self._commands = torch.zeros(self.num_envs, 3, device=self.device)
+        self._joint_dof_idx, _ = self.robots["robot_1"].find_joints(".*")
+
+        # Logging
+        self._episode_sums = {
+            key: torch.zeros(self.num_envs, dtype=torch.float32, device=self.device)
+            for key in [
+                "track_lin_vel_xy_exp",
+                "track_ang_vel_z_exp",
+            ]
+        }
+
+        self.base_ids = {}
+        self.feet_ids = {}
+        self.undesired_body_contact_ids = {}
+
+        for idx, (robot_id, contact_sensor) in enumerate(self.contact_sensors.items()):
+            _base_id, _ = contact_sensor.find_bodies(self.base_bodies[idx])
+            _feet_ids, _ = contact_sensor.find_bodies(".*FOOT")
+            _undesired_contact_body_ids, _ = contact_sensor.find_bodies(".*THIGH")
+            self.base_ids[robot_id] = _base_id
+            self.feet_ids[robot_id] = _feet_ids
+            self.undesired_body_contact_ids[robot_id] = _undesired_contact_body_ids
+
+        self.potentials = torch.zeros(self.num_envs, dtype=torch.float32, device=self.sim.device)
+        self.prev_potentials = torch.zeros_like(self.potentials)
+        self.targets = torch.tensor([1000, 0, 0], dtype=torch.float32, device=self.sim.device).repeat(
+            (self.num_envs, 1)
+        )
+        self.joint_gears = torch.tensor(self.cfg.joint_gears, dtype=torch.float32, device=self.sim.device)
+        self.targets = torch.tensor([1000, 0, 0], dtype=torch.float32, device=self.sim.device).repeat(
+            (self.num_envs, 1)
+        )
+        self.targets += self.scene.env_origins
+        self.heading_vec = torch.tensor([1, 0, 0], dtype=torch.float32, device=self.sim.device).repeat(
+            (self.num_envs, 1)
+        )
+        self.up_vec = torch.tensor([0, 0, 1], dtype=torch.float32, device=self.sim.device).repeat((self.num_envs, 1))
+        self.start_rotation = torch.tensor([1, 0, 0, 0], device=self.sim.device, dtype=torch.float32)
+        self.inv_start_rot = quat_conjugate(self.start_rotation).repeat((self.num_envs, 1))
+        self.basis_vec0 = self.heading_vec.clone()
+        self.basis_vec1 = self.up_vec.clone()
+
+    def _setup_scene(self):
+        self.num_robots = sum(1 for key in self.cfg.__dict__.keys() if "robot_" in key)
+        self.robots = {}
+        self.contact_sensors = {}
+        self.height_scanners = {}
+        self.my_visualizer = define_markers()
+        self.object = RigidObject(self.cfg.cfg_rec_prism)
+
+        self.scene.rigid_objects["object"] = self.object
+
+        for i in range(self.num_robots):
+            robot_id = f"robot_{i}"
+            if robot_id in self.cfg.__dict__:
+                self.robots[f"robot_{i}"] = Articulation(self.cfg.__dict__["robot_" + str(i)])
+                self.scene.articulations[f"robot_{i}"] = self.robots[f"robot_{i}"]
+
+            contact_sensor_id = "contact_sensor_" + str(i)
+
+            if contact_sensor_id in self.cfg.__dict__:
+                self.contact_sensors[f"robot_{i}"] = ContactSensor(self.cfg.__dict__["contact_sensor_" + str(i)])
+                self.scene.sensors[f"robot_{i}"] = self.contact_sensors[f"robot_{i}"]
+
+        self.cfg.terrain.num_envs = self.scene.cfg.num_envs
+        self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing
+        self._terrain = self.cfg.terrain.class_type(self.cfg.terrain)
+        # clone, filter, and replicate
+        self.scene.clone_environments(copy_from_source=False)
+        self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path])
+        # add lights
+        light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75))
+        light_cfg.func("/World/Light", light_cfg)
+
+    def _pre_physics_step(self, actions: dict):
+        # We need to process the actions for each scene independently
+        self.processed_actions = copy.deepcopy(actions)
+
+        robot_id = "robot_0"
+        robot_action_space = self.action_spaces[robot_id].shape[0]
+        self.actions[robot_id] = actions[robot_id][:, :robot_action_space].clone()
+        self.processed_actions[robot_id] = (
+            self.cfg.anymal_action_scale * self.actions[robot_id] + self.robots[robot_id].data.default_joint_pos
+        )
+
+        robot_id = "robot_1"
+        self.actions[robot_id] = actions[robot_id].clone()
+
+    def _get_anymal_fallen(self):
+        agent_dones = []
+        robot = self.robots["robot_0"]
+        died = robot.data.body_com_pos_w[:, 0, 2].view(-1) < self.cfg.anymal_min_z_pos
+        agent_dones.append(died)
+
+        robot = self.robots["robot_1"]
+        died = robot.data.body_com_pos_w[:, 0, 2].view(-1) < self.cfg.h1_min_z_pos
+        agent_dones.append(died)
+
+        return torch.any(torch.stack(agent_dones), dim=0)
+
+    def _apply_action(self):
+
+        robot_id = "robot_0"
+        self.robots[robot_id].set_joint_position_target(self.processed_actions[robot_id])
+
+        robot_id = "robot_1"
+        forces = self.cfg.h1_action_scale * self.joint_gears * self.actions[robot_id]
+        self.robots[robot_id].set_joint_effort_target(forces, joint_ids=self._joint_dof_idx)
+
+    def _compute_intermediate_values(self):
+        self.torso_position, self.torso_rotation = (
+            self.robots["robot_1"].data.root_link_pos_w,
+            self.robots["robot_1"].data.root_link_quat_w,
+        )
+        self.velocity, self.ang_velocity = (
+            self.robots["robot_1"].data.root_com_lin_vel_w,
+            self.robots["robot_1"].data.root_com_ang_vel_w,
+        )
+        self.dof_pos, self.dof_vel = self.robots["robot_1"].data.joint_pos, self.robots["robot_1"].data.joint_vel
+
+        (
+            self.up_proj,
+            self.heading_proj,
+            self.up_vec,
+            self.heading_vec,
+            self.vel_loc,
+            self.angvel_loc,
+            self.roll,
+            self.pitch,
+            self.yaw,
+            self.angle_to_target,
+            self.dof_pos_scaled,
+            self.prev_potentials,
+            self.potentials,
+        ) = compute_intermediate_values(
+            self.targets,
+            self.torso_position,
+            self.torso_rotation,
+            self.velocity,
+            self.ang_velocity,
+            self.dof_pos,
+            self.robots["robot_1"].data.soft_joint_pos_limits[0, :, 0],
+            self.robots["robot_1"].data.soft_joint_pos_limits[0, :, 1],
+            self.inv_start_rot,
+            self.basis_vec0,
+            self.basis_vec1,
+            self.potentials,
+            self.prev_potentials,
+            self.cfg.sim.dt,
+        )
+
+    def _get_observations(self) -> dict:
+        self.previous_actions = copy.deepcopy(self.actions)
+
+        obs = {}
+
+        robot_id = "robot_0"
+        robot = self.robots[robot_id]
+        # anymal_commands = torch.zeros_like(self._commands)
+        obs[robot_id] = torch.cat(
+            [
+                tensor
+                for tensor in (
+                    robot.data.root_com_lin_vel_b,
+                    robot.data.root_com_ang_vel_b,
+                    robot.data.projected_gravity_b,
+                    self._commands,
+                    robot.data.joint_pos - robot.data.default_joint_pos,
+                    robot.data.joint_vel,
+                    None,
+                    self.actions[robot_id],
+                )
+                if tensor is not None
+            ],
+            dim=-1,
+        )
+
+        robot_id = "robot_1"
+        robot = self.robots[robot_id]
+        obs[robot_id] = torch.cat(
+            (
+                self.torso_position[:, 2].view(-1, 1),
+                self.vel_loc,
+                self.angvel_loc * self.cfg.angular_velocity_scale,
+                normalize_angle(self.yaw).unsqueeze(-1),
+                normalize_angle(self.roll).unsqueeze(-1),
+                normalize_angle(self.angle_to_target).unsqueeze(-1),
+                self.up_proj.unsqueeze(-1),
+                self.heading_proj.unsqueeze(-1),
+                self.dof_pos_scaled,
+                self.dof_vel * self.cfg.dof_vel_scale,
+                self.actions[robot_id],
+                self._commands,
+            ),
+            dim=-1,
+        )
+        return obs
+
+    def get_y_euler_from_quat(self, quaternion):
+        w, x, y, z = quaternion[:, 0], quaternion[:, 1], quaternion[:, 2], quaternion[:, 3]
+        y_euler_angle = torch.arcsin(2 * (w * y - z * x))
+        return y_euler_angle
+
+    def _draw_markers(self, command):
+        xy_commands = command.clone()
+        z_commands = xy_commands[:, 2].clone()
+        xy_commands[:, 2] = 0
+
+        marker_ids = torch.concat(
+            [
+                0 * torch.zeros(2 * self._commands.shape[0]),
+                1 * torch.ones(self._commands.shape[0]),
+                2 * torch.ones(self._commands.shape[0]),
+                3 * torch.ones(self._commands.shape[0]),
+            ],
+            dim=0,
+        )
+
+        bar_pos = self.object.data.body_com_pos_w.squeeze(1).clone()
+        bar_yaw = self.object.data.root_com_ang_vel_b[:, 2].clone()
+
+        scale1 = torch.ones((self._commands.shape[0], 3), device=self.device)
+        scale1[:, 0] = torch.abs(z_commands)
+
+        scale2 = torch.ones((self._commands.shape[0], 3), device=self.device)
+        scale2[:, 0] = torch.abs(bar_yaw)
+
+        offset1 = torch.zeros((self._commands.shape[0], 3), device=self.device)
+        offset1[:, 1] = 0
+
+        offset2 = torch.zeros((self._commands.shape[0], 3), device=self.device)
+        offset2[:, 1] = 0
+
+        _90 = (-3.14 / 2) * torch.ones(self._commands.shape[0]).to(self.device)
+
+        marker_orientations = quat_from_angle_axis(
+            torch.concat(
+                [
+                    torch.zeros(3 * self._commands.shape[0]).to(self.device),
+                    torch.sign(z_commands) * _90,
+                    torch.sign(bar_yaw) * _90,
+                ],
+                dim=0,
+            ),
+            torch.tensor([0.0, 1.0, 0.0], device=self.device),
+        )
+
+        marker_scales = torch.concat(
+            [torch.ones((3 * self._commands.shape[0], 3), device=self.device), scale1, scale2], dim=0
+        )
+
+        # obj_vel = self.object.data.root_com_lin_vel_b.clone()
+        # obj_vel[:, 2] = 0
+
+        obj_vel = self.object.data.root_com_lin_vel_b
+        obj_vel[:, 2] = 0
+
+        offset = torch.tensor([0, 2, 0], device=self.device)
+
+        marker_locations = torch.concat(
+            [
+                bar_pos + offset,
+                bar_pos + xy_commands + offset,
+                bar_pos + obj_vel + offset,
+                bar_pos + offset1 + offset,
+                bar_pos + offset2 + offset,
+            ],
+            dim=0,
+        )
+
+        self.my_visualizer.visualize(
+            marker_locations, marker_orientations, scales=marker_scales, marker_indices=marker_ids
+        )
+
+    def _get_rewards(self) -> dict:
+        reward = {}
+
+        bar_commands = torch.stack([-self._commands[:, 1], self._commands[:, 0], self._commands[:, 2]]).t()
+        self._draw_markers(bar_commands)
+        obj_xy_vel = self.object.data.root_com_lin_vel_b[:, :2]
+
+        # linear velocity tracking
+        lin_vel_error = torch.sum(torch.square(bar_commands[:, :2] - obj_xy_vel), dim=1)  # changing this to the bar
+        lin_vel_error_mapped = torch.exp(-lin_vel_error)
+
+        # angular velocity tracking
+        yaw_rate_error = torch.square(self._commands[:, 2] - self.object.data.root_com_ang_vel_b[:, 2])
+        yaw_rate_error_mapped = torch.exp(-yaw_rate_error)
+
+        rewards = {
+            "track_lin_vel_xy_exp": lin_vel_error_mapped * self.cfg.lin_vel_reward_scale * self.step_dt,
+            "track_ang_vel_z_exp": yaw_rate_error_mapped * self.cfg.yaw_rate_reward_scale * self.step_dt,
+        }
+        reward = torch.sum(torch.stack(list(rewards.values())), dim=0)
+
+        for key, val in rewards.items():
+            self._episode_sums[key] += val
+
+        return {"robot_0": reward, "robot_1": reward}
+
+    def _get_too_far_away(self):
+        anymal_pos = self.robots["robot_0"].data.body_com_pos_w[:, 0, :]
+        h1_pos = self.robots["robot_1"].data.body_com_pos_w[:, 0, :]
+
+        box_pos = self.object.data.body_com_pos_w[:, 0, :]
+
+        anymal_too_far = (
+            torch.sqrt(torch.square(anymal_pos[:, 0] - box_pos[:, 0]) + torch.square(anymal_pos[:, 1] - box_pos[:, 1]))
+            > 3
+        )
+        h1_too_far = (
+            torch.sqrt(torch.square(h1_pos[:, 0] - box_pos[:, 0]) + torch.square(h1_pos[:, 1] - box_pos[:, 1])) > 3
+        )
+
+        return torch.logical_or(anymal_too_far, h1_too_far)
+
+    def _get_dones(self) -> tuple[dict, dict]:
+        self._compute_intermediate_values()
+        time_out = self.episode_length_buf >= self.max_episode_length - 1
+        h1_died = self.torso_position[:, 2] < self.cfg.termination_height
+        anymal_fallen = self._get_anymal_fallen()
+        too_far = self._get_too_far_away()
+        dones = torch.logical_or(h1_died, anymal_fallen)
+        dones = torch.logical_or(dones, too_far)
+        return {key: time_out for key in self.robots.keys()}, {key: dones for key in self.robots.keys()}
+
+    def _reset_idx(self, env_ids: torch.Tensor | None):
+        super()._reset_idx(env_ids)
+        object_default_state = self.object.data.default_root_state.clone()[env_ids]
+        object_default_state[:, 0:3] = object_default_state[:, 0:3] + self.scene.env_origins[env_ids]
+        self.object.write_root_state_to_sim(object_default_state, env_ids)
+        self.object.reset(env_ids)
+
+        if len(env_ids) == self.num_envs:
+            # Spread out the resets to avoid spikes in training when many environments reset at a similar time
+            self.episode_length_buf[:] = torch.randint_like(self.episode_length_buf, high=int(self.max_episode_length))
+
+        # Joint position command (deviation from default joint positions)
+        for agent, action_space in self.cfg.action_spaces.items():
+            self.actions[agent][env_ids] = torch.zeros(env_ids.shape[0], action_space, device=self.device)
+            self.previous_actions[agent][env_ids] = torch.zeros(env_ids.shape[0], action_space, device=self.device)
+
+        # X/Y linear velocity and yaw angular velocity commands
+        self._commands[env_ids] = torch.zeros_like(self._commands[env_ids]).uniform_(-1.0, 1.0)
+        self._commands[env_ids, 0] = torch.zeros_like(self._commands[env_ids, 0]).uniform_(0.5, 1.0)
+
+        # reset idx for anymal #
+        robot = self.robots["robot_0"]
+        if env_ids is None or len(env_ids) == self.num_envs:
+            env_ids = robot._ALL_INDICES
+        robot.reset(env_ids)
+
+        joint_pos = robot.data.default_joint_pos[env_ids]
+        joint_vel = robot.data.default_joint_vel[env_ids]
+        default_root_state = robot.data.default_root_state[env_ids]
+        default_root_state[:, :3] += self._terrain.env_origins[env_ids]
+        robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids)
+        robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids)
+        robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids)
+
+        robot = self.robots["robot_1"]
+        if env_ids is None or len(env_ids) == self.num_envs:
+            env_ids = robot._ALL_INDICES
+        robot.reset(env_ids)
+
+        # Reset robot state
+        to_target = self.targets[env_ids] - default_root_state[:, :3]
+        to_target[:, 2] = 0.0
+        self.potentials[env_ids] = -torch.norm(to_target, p=2, dim=-1) / self.cfg.sim.dt
+        self._compute_intermediate_values()
+        joint_pos = robot.data.default_joint_pos[env_ids]
+        joint_vel = robot.data.default_joint_vel[env_ids]
+        default_root_state = robot.data.default_root_state[env_ids]
+        default_root_state[:, :3] += self._terrain.env_origins[env_ids]
+        robot.write_root_pose_to_sim(default_root_state[:, :7], env_ids)
+        robot.write_root_velocity_to_sim(default_root_state[:, 7:], env_ids)
+        robot.write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids)
+        # Logging
+        extras = dict()
+        for key in self._episode_sums.keys():
+            episodic_sum_avg = torch.mean(self._episode_sums[key][env_ids])
+            extras["Episode_Reward/" + key] = episodic_sum_avg / self.max_episode_length_s
+            self._episode_sums[key][env_ids] = 0.0
+        self.extras["log"] = dict()
+        self.extras["log"].update(extras)
+        extras = dict()
+
+@torch.jit.script
+def compute_intermediate_values(
+    targets: torch.Tensor,
+    torso_position: torch.Tensor,
+    torso_rotation: torch.Tensor,
+    velocity: torch.Tensor,
+    ang_velocity: torch.Tensor,
+    dof_pos: torch.Tensor,
+    dof_lower_limits: torch.Tensor,
+    dof_upper_limits: torch.Tensor,
+    inv_start_rot: torch.Tensor,
+    basis_vec0: torch.Tensor,
+    basis_vec1: torch.Tensor,
+    potentials: torch.Tensor,
+    prev_potentials: torch.Tensor,
+    dt: float,
+):
+    to_target = targets - torso_position
+    to_target[:, 2] = 0.0
+    torso_quat, up_proj, heading_proj, up_vec, heading_vec = compute_heading_and_up(
+        torso_rotation, inv_start_rot, to_target, basis_vec0, basis_vec1, 2
+    )
+
+    vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target = compute_rot(
+        torso_quat, velocity, ang_velocity, targets, torso_position
+    )
+
+    dof_pos_scaled = torch_utils.maths.unscale(dof_pos, dof_lower_limits, dof_upper_limits)
+
+    to_target = targets - torso_position
+    to_target[:, 2] = 0.0
+    prev_potentials[:] = potentials
+    potentials = -torch.norm(to_target, p=2, dim=-1) / dt
+
+    return (
+        up_proj,
+        heading_proj,
+        up_vec,
+        heading_vec,
+        vel_loc,
+        angvel_loc,
+        roll,
+        pitch,
+        yaw,
+        angle_to_target,
+        dof_pos_scaled,
+        prev_potentials,
+        potentials,
+    )

From 978a2d1bd8ce4c7a4b85efabd13b9ddfdc04d01d Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 15:23:48 -0600
Subject: [PATCH 02/11] ran ./isaaclab.sh --format

---
 scripts/reinforcement_learning/harl/play.py   |  5 +++
 .../harl/play_adversarial.py                  | 20 ++++++----
 scripts/reinforcement_learning/harl/train.py  | 37 ++++++++++++-------
 .../multi_agent_coordination/__init__.py      |  5 +++
 .../agents/__init__.py                        |  5 +++
 .../agents/harl_happo_cfg.yaml                |  5 +++
 .../anymal_c_multi_agent_bar_env.py           | 19 +++++-----
 .../h1_anymal_push_env.py                     |  8 ++++
 8 files changed, 73 insertions(+), 31 deletions(-)

diff --git a/scripts/reinforcement_learning/harl/play.py b/scripts/reinforcement_learning/harl/play.py
index fe8a31a0e6a..7ec343fa0e5 100644
--- a/scripts/reinforcement_learning/harl/play.py
+++ b/scripts/reinforcement_learning/harl/play.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
diff --git a/scripts/reinforcement_learning/harl/play_adversarial.py b/scripts/reinforcement_learning/harl/play_adversarial.py
index 387509bd302..89e64541fb7 100644
--- a/scripts/reinforcement_learning/harl/play_adversarial.py
+++ b/scripts/reinforcement_learning/harl/play_adversarial.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
@@ -6,13 +11,14 @@
 """Train an algorithm."""
 
 import argparse
+import pprint
 
 # import numpy as np
 import sys
 import torch
 from tqdm import tqdm
+
 from isaaclab.app import AppLauncher
-import pprint
 
 parser = argparse.ArgumentParser(description="Train an RL agent with HARL.")
 parser.add_argument(
@@ -139,24 +145,24 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
         envs_completed += curr_envs_completed
 
         if curr_envs_completed > 0:
-            for k,v in runner.env.log_info.items():
+            for k, v in runner.env.log_info.items():
                 if k not in log_infos:
                     log_infos[k] = 0.0
-                log_infos[k] += (v * curr_envs_completed)
+                log_infos[k] += v * curr_envs_completed
 
         masks = torch.ones((args["num_envs"], runner.num_agents, 1), dtype=torch.float32, device="cuda:0")
         masks[dones_env] = 0.0
         rnn_states[dones_env] = torch.zeros(
-            ((dones_env).sum(), runner.num_agents, runner.recurrent_n, runner.rnn_hidden_size), # type: ignore
+            ((dones_env).sum(), runner.num_agents, runner.recurrent_n, runner.rnn_hidden_size),  # type: ignore
             dtype=torch.float32,
             device="cuda:0",
-        ) # type: ignore
+        )  # type: ignore
 
-        num_steps = args["num_envs"] * runner.env.unwrapped.sim._number_of_steps 
+        num_steps = args["num_envs"] * runner.env.unwrapped.sim._number_of_steps
         pbar.update(num_steps - pbar.n)
         if num_steps >= args["num_env_steps"]:
             break
-        
+
     runner.env.close()
 
     for k in log_infos.keys():
diff --git a/scripts/reinforcement_learning/harl/train.py b/scripts/reinforcement_learning/harl/train.py
index 8f7bf6b4afa..6f575b27a86 100644
--- a/scripts/reinforcement_learning/harl/train.py
+++ b/scripts/reinforcement_learning/harl/train.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
@@ -26,23 +31,28 @@
 parser.add_argument("--num_env_steps", type=int, default=None, help="RL Policy training iterations.")
 parser.add_argument("--dir", type=str, default=None, help="folder with trained models")
 parser.add_argument("--debug", action="store_true", help="whether to run in debug mode for visualization")
-parser.add_argument("--adversarial_training_mode", default="parallel", choices=["parallel", "ladder", "leapfrog"], help="the mode type for adversarial training,\
-                     note on ladder training with teams that are composed of heterogenous agents, the two teams must place the robots in the same order in their environment \
-                    for ladder to work")
-parser.add_argument("--adversarial_training_iterations", default=50_000_000, type=int,help="the number of iterations to swap training for adversarial modes like ladder and leapfrog")
+parser.add_argument(
+    "--adversarial_training_mode",
+    default="parallel",
+    choices=["parallel", "ladder", "leapfrog"],
+    help=(
+        "the mode type for adversarial training,                     note on ladder training with teams that are"
+        " composed of heterogeneous agents, the two teams must place the robots in the same order in their environment  "
+        "                   for ladder to work"
+    ),
+)
+parser.add_argument(
+    "--adversarial_training_iterations",
+    default=50_000_000,
+    type=int,
+    help="the number of iterations to swap training for adversarial modes like ladder and leapfrog",
+)
 
 parser.add_argument(
     "--algorithm",
     type=str,
     default="happo",
-    choices=[
-        "happo",
-        "hatrpo",
-        "haa2c",
-        "mappo",
-        "mappo_unshare",
-        "happo_adv"
-    ],
+    choices=["happo", "hatrpo", "haa2c", "mappo", "mappo_unshare", "happo_adv"],
     help="Algorithm name. Choose from: happo, hatrpo, haa2c, mappo, and mappo_unshare.",
 )
 
@@ -121,7 +131,6 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     env_args["headless"] = args["headless"]
     env_args["debug"] = args["debug"]
 
-
     # create runner
 
     runner = RUNNER_REGISTRY[args["algo"]](args, algo_args, env_args)
@@ -130,5 +139,5 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
 
 
 if __name__ == "__main__":
-    main() # type: ignore
+    main()  # type: ignore
     simulation_app.close()
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
index bfd24f9a1d3..dfc32637c1a 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
index e75ca2bc3f9..2227acce701 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml
index 89208957ac9..fd72a6f48c1 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/harl_happo_cfg.yaml
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # This is the configuration file for the HAPPO algorithm.
 seed:
   # whether to use the specified seed
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
index 46060ecad65..7fe6672cc30 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
@@ -187,7 +192,7 @@ class AnymalCMultiAgentBarEnvCfg(DirectMARLEnvCfg):
     lin_vel_reward_scale = 1.0
     yaw_rate_reward_scale = 1.0
 
-    bar_z_min_pos = .6
+    bar_z_min_pos = 0.6
 
     anymal_min_z_pos = 0.1
     max_bar_roll_angle_rad = 1
@@ -196,9 +201,7 @@ class AnymalCMultiAgentBarEnvCfg(DirectMARLEnvCfg):
 class AnymalCMultiAgentBar(DirectMARLEnv):
     cfg: AnymalCMultiAgentBarEnvCfg
 
-    def __init__(
-        self, cfg: AnymalCMultiAgentBarEnvCfg, render_mode: str | None = None, **kwargs
-    ):
+    def __init__(self, cfg: AnymalCMultiAgentBarEnvCfg, render_mode: str | None = None, **kwargs):
         super().__init__(cfg, render_mode, **kwargs)
         # Joint position command (deviation from default joint positions)
 
@@ -372,9 +375,7 @@ def _get_rewards(self) -> dict:
         self._draw_markers(bar_commands)
 
         # xy linear velocity tracking
-        lin_vel_error = torch.sum(
-            torch.square(bar_commands[:, :2] - self.object.data.root_com_lin_vel_b[:, :2]), dim=1
-        )
+        lin_vel_error = torch.sum(torch.square(bar_commands[:, :2] - self.object.data.root_com_lin_vel_b[:, :2]), dim=1)
         lin_vel_error_mapped = torch.exp(-lin_vel_error)
 
         # yaw rate tracking
@@ -391,7 +392,7 @@ def _get_rewards(self) -> dict:
         for key, value in rewards.items():
             self._episode_sums[key] += value
 
-        return {"robot_0":reward, "robot_1":reward}
+        return {"robot_0": reward, "robot_1": reward}
 
     def _get_anymal_fallen(self):
         agent_dones = []
@@ -463,5 +464,3 @@ def _reset_idx(self, env_ids: torch.Tensor):
             self._episode_sums[key][env_ids] = 0.0
         self.extras["log"] = dict()
         self.extras["log"].update(extras)
-
-
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
index 06d1e4a4582..9041eb4debc 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
@@ -203,6 +208,7 @@ class HeterogeneousPushMultiAgentEnvCfg(DirectMARLEnvCfg):
         50.0,  # right_elbow
     ]
 
+
 def define_markers() -> VisualizationMarkers:
     marker_cfg = VisualizationMarkersCfg(
         prim_path="/Visuals/myMarkers",
@@ -229,6 +235,7 @@ def define_markers() -> VisualizationMarkers:
     )
     return VisualizationMarkers(marker_cfg)
 
+
 class HeterogeneousPushMultiAgentEnv(DirectMARLEnv):
     cfg: HeterogeneousPushMultiAgentEnvCfg
 
@@ -638,6 +645,7 @@ def _reset_idx(self, env_ids: torch.Tensor | None):
         self.extras["log"].update(extras)
         extras = dict()
 
+
 @torch.jit.script
 def compute_intermediate_values(
     targets: torch.Tensor,

From a7ee96675be831cb626b695a7bf8fbadcc1b8861 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 15:24:41 -0600
Subject: [PATCH 03/11] formatting

---
 scripts/reinforcement_learning/harl/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/reinforcement_learning/harl/train.py b/scripts/reinforcement_learning/harl/train.py
index 6f575b27a86..6c19c7aeac2 100644
--- a/scripts/reinforcement_learning/harl/train.py
+++ b/scripts/reinforcement_learning/harl/train.py
@@ -37,8 +37,8 @@
     choices=["parallel", "ladder", "leapfrog"],
     help=(
         "the mode type for adversarial training,                     note on ladder training with teams that are"
-        " composed of heterogeneous agents, the two teams must place the robots in the same order in their environment  "
-        "                   for ladder to work"
+        " composed of heterogeneous agents, the two teams must place the robots in the same order in their environment "
+        "                    for ladder to work"
     ),
 )
 parser.add_argument(

From 2576036d638cfff40cf5a943e573a2631ce0f0f8 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 15:27:58 -0600
Subject: [PATCH 04/11] more formatting

---
 scripts/reinforcement_learning/harl/train.py                   | 2 +-
 .../multi_agent_coordination/anymal_c_multi_agent_bar_env.py   | 1 -
 .../direct/multi_agent_coordination/h1_anymal_push_env.py      | 3 +--
 3 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/scripts/reinforcement_learning/harl/train.py b/scripts/reinforcement_learning/harl/train.py
index 6c19c7aeac2..f05199da3ee 100644
--- a/scripts/reinforcement_learning/harl/train.py
+++ b/scripts/reinforcement_learning/harl/train.py
@@ -114,7 +114,7 @@ def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agen
     env_args["config"] = env_cfg
     hms_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
     env_args["video_settings"] = {
-        "video": True if args["video"] else False,
+        "video": bool(args["video"]),
         "video_length": args["video_length"],
         "video_interval": args["video_interval"],
         "log_dir": os.path.join(
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
index 7fe6672cc30..6216f0d7e17 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
@@ -32,7 +32,6 @@
 # Pre-defined configs
 ##
 from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
-from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG  # isort: skip
 
 
 @configclass
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
index 9041eb4debc..16f61c689f6 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
@@ -24,7 +24,7 @@
 from isaaclab.managers import SceneEntityCfg
 from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg
 from isaaclab.scene import InteractiveSceneCfg
-from isaaclab.sensors import ContactSensor, ContactSensorCfg, RayCasterCfg, patterns
+from isaaclab.sensors import ContactSensor, ContactSensorCfg
 from isaaclab.sim import SimulationCfg
 from isaaclab.terrains import TerrainImporterCfg
 from isaaclab.utils import configclass
@@ -36,7 +36,6 @@
 ##
 from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
 from isaaclab_assets.robots.unitree import H1_CFG  # isort: skip
-from isaaclab.terrains.config.rough import ROUGH_TERRAINS_CFG  # isort: skip
 
 
 def normalize_angle(x):

From e186feb65c196c47356621e6260d6953119787b1 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 15:35:34 -0600
Subject: [PATCH 05/11] added contributors and removed script unrelated to
 multi agent coordination

---
 CONTRIBUTORS.md                               |   3 +
 .../harl/play_adversarial.py                  | 177 ------------------
 2 files changed, 3 insertions(+), 177 deletions(-)
 delete mode 100644 scripts/reinforcement_learning/harl/play_adversarial.py

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index ebfc174f0a7..2cedc80075e 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -152,6 +152,9 @@ Guidelines for modifications:
 * Zoe McCarthy
 * David Leon
 * Song Yi
+* Jacob Haight
+* Isaac Peterson
+* Christopher Allred
 
 ## Acknowledgements
 
diff --git a/scripts/reinforcement_learning/harl/play_adversarial.py b/scripts/reinforcement_learning/harl/play_adversarial.py
deleted file mode 100644
index 89e64541fb7..00000000000
--- a/scripts/reinforcement_learning/harl/play_adversarial.py
+++ /dev/null
@@ -1,177 +0,0 @@
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
-"""Train an algorithm."""
-
-import argparse
-import pprint
-
-# import numpy as np
-import sys
-import torch
-from tqdm import tqdm
-
-from isaaclab.app import AppLauncher
-
-parser = argparse.ArgumentParser(description="Train an RL agent with HARL.")
-parser.add_argument(
-    "--algorithm",
-    type=str,
-    default="happo",
-    choices=[
-        "happo",
-        "hatrpo",
-        "haa2c",
-        "haddpg",
-        "hatd3",
-        "hasac",
-        "had3qn",
-        "maddpg",
-        "matd3",
-        "mappo",
-        "happo_adv",
-    ],
-    help="Algorithm name. Choose from: happo, hatrpo, haa2c, haddpg, hatd3, hasac, had3qn, maddpg, matd3, mappo.",
-)
-parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
-parser.add_argument("--task", type=str, default=None, help="Name of the task.")
-parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
-parser.add_argument("--num_env_steps", type=int, default=None, help="RL Policy training iterations.")
-parser.add_argument("--dir", type=str, default=None, help="folder with trained models")
-parser.add_argument("--debug", action="store_true", help="whether to run in debug mode for visualization")
-
-# append AppLauncher cli args
-AppLauncher.add_app_launcher_args(parser)
-# parse the arguments
-args_cli, hydra_args = parser.parse_known_args()
-
-# clear out sys.argv for Hydra
-sys.argv = [sys.argv[0]] + hydra_args
-
-# launch omniverse app
-app_launcher = AppLauncher(args_cli)
-simulation_app = app_launcher.app
-
-from harl.runners import RUNNER_REGISTRY
-
-from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
-
-import isaaclab_tasks  # noqa: F401
-from isaaclab_tasks.utils.hydra import hydra_task_config
-
-algorithm = args_cli.algorithm.lower()
-agent_cfg_entry_point = f"harl_{algorithm}_cfg_entry_point"
-
-
-@hydra_task_config(args_cli.task, agent_cfg_entry_point)
-def main(env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg, agent_cfg: dict):
-    args = args_cli.__dict__
-
-    args["env"] = "isaaclab"
-    args["algo"] = args["algorithm"]
-    args["exp_name"] = "play"
-
-    algo_args = agent_cfg
-
-    algo_args["eval"]["use_eval"] = False
-    algo_args["render"]["use_render"] = True
-    algo_args["train"]["model_dir"] = args["dir"]
-
-    env_args = {}
-    env_cfg.scene.num_envs = args["num_envs"]
-    env_args["task"] = args["task"]
-    env_args["config"] = env_cfg
-    env_args["video_settings"] = {}
-    env_args["video_settings"]["video"] = False
-    env_args["headless"] = args["headless"]
-    env_args["debug"] = True
-
-    # create runner
-    runner = RUNNER_REGISTRY[args["algo"]](args, algo_args, env_args)
-
-    obs, _, _ = runner.env.reset()
-
-    max_action_space = 0
-
-    for agent_id, obs_space in runner.env.action_space.items():
-        if type(obs_space) is dict:
-            for agent_id, agent_obs in obs_space.items():
-                if agent_obs.shape[0] > max_action_space:
-                    max_action_space = agent_obs.shape[0]
-        elif obs_space.shape[0] > max_action_space:
-            max_action_space = obs_space.shape[0]
-
-    actions = torch.zeros((args["num_envs"], runner.num_agents, max_action_space), dtype=torch.float32, device="cuda:0")
-    rnn_states = torch.zeros(
-        (
-            args["num_envs"],
-            runner.num_agents,
-            runner.recurrent_n,
-            runner.rnn_hidden_size,
-        ),
-        dtype=torch.float32,
-        device="cuda:0",
-    )
-    masks = torch.ones(
-        (args["num_envs"], runner.num_agents, 1),
-        dtype=torch.float32,
-    )
-    log_infos = {}
-    envs_completed = 0
-    pbar = tqdm(total=args["num_env_steps"], desc="Playing")
-    while simulation_app.is_running():
-        with torch.inference_mode():
-            for team, agents_obs in obs.items():
-                for agent_id, agent_obs in agents_obs.items():
-                    agent_num = runner.env.env._agent_map[agent_id]
-                    action, _, rnn_state = runner.actors[team][agent_id].get_actions(
-                        agent_obs, rnn_states[:, agent_num, :], masks[:, agent_num, :], None, None
-                    )
-                    action_space = action.shape[1]
-                    actions[:, agent_num, :action_space] = action
-                    rnn_states[:, agent_num, :] = rnn_state
-
-        # We don't want to randomize the reset buffer when evaluating
-        obs, _, _, dones, _, _ = runner.env.step(actions)
-        dones_env = torch.all(dones, dim=1)
-        curr_envs_completed = dones_env.sum().item()
-        envs_completed += curr_envs_completed
-
-        if curr_envs_completed > 0:
-            for k, v in runner.env.log_info.items():
-                if k not in log_infos:
-                    log_infos[k] = 0.0
-                log_infos[k] += v * curr_envs_completed
-
-        masks = torch.ones((args["num_envs"], runner.num_agents, 1), dtype=torch.float32, device="cuda:0")
-        masks[dones_env] = 0.0
-        rnn_states[dones_env] = torch.zeros(
-            ((dones_env).sum(), runner.num_agents, runner.recurrent_n, runner.rnn_hidden_size),  # type: ignore
-            dtype=torch.float32,
-            device="cuda:0",
-        )  # type: ignore
-
-        num_steps = args["num_envs"] * runner.env.unwrapped.sim._number_of_steps
-        pbar.update(num_steps - pbar.n)
-        if num_steps >= args["num_env_steps"]:
-            break
-
-    runner.env.close()
-
-    for k in log_infos.keys():
-        log_infos[k] /= envs_completed if envs_completed > 0 else 1
-
-    pbar.close()
-    pprint.pprint(log_infos)
-
-
-if __name__ == "__main__":
-    main()
-    simulation_app.close()

From c23468a8e490fde9776c23a355b358c237c92fb5 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 18:28:32 -0600
Subject: [PATCH 06/11] added env for training anymal c with happo to use in
 the multi agent bar env

---
 CONTRIBUTORS.md                               |  21 +
 .../best_model/actor_agent_robot_0.pt         | Bin 105312 -> 0 bytes
 .../best_model/actor_agent_robot_1.pt         | Bin 105312 -> 0 bytes
 .../best_model/critic_agent.pt                | Bin 124121 -> 0 bytes
 .../best_model/value_normalizer.pt            | Bin 1379 -> 0 bytes
 .../progress.txt                              |   0
 .../direct/anymal_c/__init__.py               |  10 +
 .../direct/anymal_c/anymal_c_happo_env.py     | 414 ++++++++++++++++++
 8 files changed, 445 insertions(+)
 delete mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_0.pt
 delete mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_1.pt
 delete mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/critic_agent.pt
 delete mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/value_normalizer.pt
 delete mode 100644 results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/progress.txt
 create mode 100644 source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 2cedc80075e..487d8b15737 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -166,3 +166,24 @@ Guidelines for modifications:
 * Marco Hutter
 * Yan Chang
 * Yashraj Narang
+
+To see the full list of contributors, please check the revision history in the source control.
+
+Guidelines for modifications:
+
+* Please keep the **lists sorted alphabetically**.
+* Names should be added to this file as: *individual names* or *organizations*.
+* E-mail addresses are tracked elsewhere to avoid spam.
+
+## Developers
+
+* Boston Dynamics AI Institute, Inc.
+* ETH Zurich
+* NVIDIA Corporation & Affiliates
+* University of Toronto
+
+---
+
+* Antonio Serrano-Muñoz
+* Ben Johnston
+* Clemens Schwarke
\ No newline at end of file
diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_0.pt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/actor_agent_robot_0.pt
deleted file mode 100644
index 9e8ac3e0ee55485c118b9d37f4a55ba8e1ec7e2e..0000000000000000000000000000000000000000
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diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/value_normalizer.pt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/best_model/value_normalizer.pt
deleted file mode 100644
index df7dee58872a8e064783a26891af672f928fe556..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/progress.txt b/results/isaaclab/Isaac-Multi-Agent-Flat-Anymal-C-Direct-v0/happo/anymalc_adversarial/seed-00001-2025-10-29-13-17-39/progress.txt
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py
index 6c7759c049c..b33463ba335 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py
@@ -15,6 +15,16 @@
 # Register Gym environments.
 ##
 
+gym.register(
+    id="Isaac-Velocity-Flat-Anymal-C-Happo-Direct-v0",
+    entry_point=f"{__name__}.anymal_c_happo_env:AnymalCHappoEnv",
+    disable_env_checker=True,
+    kwargs={
+        "env_cfg_entry_point": f"{__name__}.anymal_c_happo_env:AnymalCHappoEnvCfg",
+        "harl_happo_cfg_entry_point": f"{agents.__name__}:harl_happo_cfg.yaml",
+    },
+)
+
 gym.register(
     id="Isaac-Velocity-Flat-Anymal-C-Direct-v0",
     entry_point=f"{__name__}.anymal_c_env:AnymalCEnv",
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
new file mode 100644
index 00000000000..fa05f0a4c60
--- /dev/null
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
@@ -0,0 +1,414 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
+from __future__ import annotations
+
+import copy
+import torch
+
+import isaaclab.envs.mdp as mdp
+import isaaclab.sim as sim_utils
+from isaaclab.assets import Articulation, ArticulationCfg
+from isaaclab.envs import DirectMARLEnv, DirectMARLEnvCfg
+from isaaclab.managers import EventTermCfg as EventTerm
+from isaaclab.managers import SceneEntityCfg
+from isaaclab.markers import VisualizationMarkers, VisualizationMarkersCfg
+from isaaclab.scene import InteractiveSceneCfg
+from isaaclab.sensors import ContactSensor, ContactSensorCfg
+from isaaclab.sim import SimulationCfg
+from isaaclab.terrains import TerrainImporterCfg
+from isaaclab.utils import configclass
+from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
+from isaaclab.utils.math import quat_from_angle_axis
+
+##
+# Pre-defined configs
+##
+from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
+
+@configclass
+class EventCfg:
+    """Configuration for randomization."""
+
+    physics_material_0 = EventTerm(
+        func=mdp.randomize_rigid_body_material,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_0", body_names=".*"),
+            "static_friction_range": (0.8, 0.8),
+            "dynamic_friction_range": (0.6, 0.6),
+            "restitution_range": (0.0, 0.0),
+            "num_buckets": 64,
+        },
+    )
+
+    add_base_mass_0 = EventTerm(
+        func=mdp.randomize_rigid_body_mass,
+        mode="startup",
+        params={
+            "asset_cfg": SceneEntityCfg("robot_0", body_names="base"),
+            "mass_distribution_params": (-5.0, 5.0),
+            "operation": "add",
+        },
+    )
+
+
+def define_markers() -> VisualizationMarkers:
+    marker_cfg = VisualizationMarkersCfg(
+        prim_path="/Visuals/myMarkers",
+        markers={
+            "sphere1": sim_utils.SphereCfg(
+                radius=0.1,
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.0)),
+            ),
+            "sphere2": sim_utils.SphereCfg(
+                radius=0.1,
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)),
+            ),
+            "arrow1": sim_utils.UsdFileCfg(
+                usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd",
+                scale=(0.1, 0.1, 1.0),
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.8, 0.0, 0.0)),
+            ),
+            "arrow2": sim_utils.UsdFileCfg(
+                usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/UIElements/arrow_x.usd",
+                scale=(0.1, 0.1, 1.0),
+                visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 1.0)),
+            ),
+        },
+    )
+    return VisualizationMarkers(marker_cfg)
+
+
+@configclass
+class AnymalCHappoEnvCfg(DirectMARLEnvCfg):
+    # env
+    episode_length_s = 20.0
+    decimation = 4
+    action_scale = 0.5
+    action_space = 12
+    action_spaces = {f"robot_{i}": 12 for i in range(1)}
+    # observation_space = 48
+    observation_space = 48
+    observation_spaces = {f"robot_{i}": 48 for i in range(1)}
+    state_space = 0
+    state_spaces = {f"robot_{i}": 0 for i in range(1)}
+    possible_agents = ["robot_0"]
+
+    # simulation
+    sim: SimulationCfg = SimulationCfg(
+        dt=1 / 200,
+        render_interval=decimation,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=0.0,
+        ),
+    )
+    terrain = TerrainImporterCfg(
+        prim_path="/World/ground",
+        terrain_type="plane",
+        collision_group=-1,
+        physics_material=sim_utils.RigidBodyMaterialCfg(
+            friction_combine_mode="multiply",
+            restitution_combine_mode="multiply",
+            static_friction=1.0,
+            dynamic_friction=1.0,
+            restitution=0.0,
+        ),
+        debug_vis=False,
+    )
+
+    # scene
+    scene: InteractiveSceneCfg = InteractiveSceneCfg(num_envs=1, env_spacing=6.0, replicate_physics=True)
+
+    # events
+    events: EventCfg = EventCfg()
+
+    # robot
+    robot_0: ArticulationCfg = ANYMAL_C_CFG.replace(prim_path="/World/envs/env_.*/Robot_0")
+    contact_sensor_0: ContactSensorCfg = ContactSensorCfg(
+        prim_path="/World/envs/env_.*/Robot_0/.*", history_length=3, update_period=0.005, track_air_time=True
+    )
+    robot_0.init_state.rot = (1.0, 0.0, 0.0, 0.0)
+    robot_0.init_state.pos = (0.0, 0.0, 0.5)
+
+    # reward scales (override from flat config)
+    flat_orientation_reward_scale = 0.0
+
+    # reward scales
+    lin_vel_reward_scale = 1.0
+    yaw_rate_reward_scale = 0.5
+    z_vel_reward_scale = -2.0
+    ang_vel_reward_scale = -0.05
+    joint_torque_reward_scale = -2.5e-5
+    joint_accel_reward_scale = -2.5e-7
+    action_rate_reward_scale = -0.01
+    feet_air_time_reward_scale = 0.5
+    undesired_contact_reward_scale = -1.0
+    flat_orientation_reward_scale = -5.0
+
+
+
+class AnymalCHappoEnv(DirectMARLEnv):
+    cfg: AnymalCHappoEnvCfg
+
+    def __init__(
+        self, cfg: AnymalCHappoEnvCfg, render_mode: str | None = None, **kwargs
+    ):
+        super().__init__(cfg, render_mode, **kwargs)
+        # Joint position command (deviation from default joint positions)
+
+        self.actions = {
+            agent: torch.zeros(self.num_envs, action_space, device=self.device)
+            for agent, action_space in self.cfg.action_spaces.items()
+        }
+        self.previous_actions = {
+            agent: torch.zeros(self.num_envs, action_space, device=self.device)
+            for agent, action_space in self.cfg.action_spaces.items()
+        }
+        # X/Y linear velocity and yaw angular velocity commands
+        # self._commands = {agent : torch.zeros(self.num_envs, 3, device=self.device) for agent in self.cfg.possible_agents}
+        self._commands = torch.zeros(self.num_envs, 3, device=self.device)
+
+        # Logging
+        self._episode_sums = {
+            key: torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
+            for key in [
+                "track_lin_vel_xy_exp",
+                "track_ang_vel_z_exp",
+                "lin_vel_z_l2",
+                "ang_vel_xy_l2",
+                "dof_torques_l2",
+                "dof_acc_l2",
+                "action_rate_l2",
+                "feet_air_time",
+                "undesired_contacts",
+                "flat_orientation_l2",
+            ]
+        }
+
+        self.base_ids = {}
+        self.feet_ids = {}
+        self.undesired_body_contact_ids = {}
+
+        for robot_id, contact_sensor in self.contact_sensors.items():
+            _base_id, _ = contact_sensor.find_bodies("base")
+            _feet_ids, _ = contact_sensor.find_bodies(".*FOOT")
+            _undesired_contact_body_ids, _ = contact_sensor.find_bodies(".*THIGH")
+            self.base_ids[robot_id] = _base_id
+            self.feet_ids[robot_id] = _feet_ids
+            self.undesired_body_contact_ids[robot_id] = _undesired_contact_body_ids
+
+    def _setup_scene(self):
+        self.num_robots = sum(1 for key in self.cfg.__dict__.keys() if "robot_" in key)
+        self.robots = {}
+        self.contact_sensors = {}
+        self.height_scanners = {}
+        self.my_visualizer = define_markers()
+
+        for i in range(self.num_robots):
+            self.robots[f"robot_{i}"] = Articulation(self.cfg.__dict__["robot_" + str(i)])
+            self.scene.articulations[f"robot_{i}"] = self.robots[f"robot_{i}"]
+            self.contact_sensors[f"robot_{i}"] = ContactSensor(self.cfg.__dict__["contact_sensor_" + str(i)])
+            self.scene.sensors[f"robot_{i}"] = self.contact_sensors[f"robot_{i}"]
+
+        self.cfg.terrain.num_envs = self.scene.cfg.num_envs
+        self.cfg.terrain.env_spacing = self.scene.cfg.env_spacing
+        self._terrain = self.cfg.terrain.class_type(self.cfg.terrain)
+        # clone, filter, and replicate
+        self.scene.clone_environments(copy_from_source=False)
+        self.scene.filter_collisions(global_prim_paths=[self.cfg.terrain.prim_path])
+        # add lights
+        light_cfg = sim_utils.DomeLightCfg(intensity=2000.0, color=(0.75, 0.75, 0.75))
+        light_cfg.func("/World/Light", light_cfg)
+
+    def _pre_physics_step(self, actions: torch.Tensor):
+        # We need to process the actions for each scene independently
+        self.processed_actions = {}
+        for robot_id, robot in self.robots.items():
+            self.actions[robot_id] = actions[robot_id].clone()
+            self.processed_actions[robot_id] = (
+                self.cfg.action_scale * self.actions[robot_id] + robot.data.default_joint_pos
+            )
+
+    def _apply_action(self):
+        for robot_id, robot in self.robots.items():
+            robot.set_joint_position_target(self.processed_actions[robot_id])
+
+    def _get_observations(self) -> dict:
+        self.previous_actions = copy.deepcopy(self.actions)
+        obs = {}
+
+        for robot_id, robot in self.robots.items():
+            obs[robot_id] = torch.cat(
+                [
+                    tensor
+                    for tensor in (
+                        robot.data.root_lin_vel_b,
+                        robot.data.root_ang_vel_b,
+                        robot.data.projected_gravity_b,
+                        self._commands,
+                        robot.data.joint_pos - robot.data.default_joint_pos,
+                        robot.data.joint_vel,
+                        self.actions[robot_id],
+                    )
+                    if tensor is not None
+                ],
+                dim=-1,
+            )
+
+        return obs
+
+    def _draw_markers(self, command):
+        xy_commands = command.clone()
+        z_commands = xy_commands[:, 2].clone()
+        xy_commands[:, 2] = 0
+
+        marker_ids = torch.concat(
+            [
+                0 * torch.zeros(2 * self._commands.shape[0]),
+                1 * torch.ones(self._commands.shape[0]),
+                2 * torch.ones(self._commands.shape[0]),
+                3 * torch.ones(self._commands.shape[0]),
+            ],
+            dim=0,
+        )
+
+        robot_pos = self.robots["robot_0"].data.root_pos_w.clone()
+        robot_yaw = self.robots["robot_0"].data.root_ang_vel_b[:, 2].clone()
+
+        scale1 = torch.ones((self._commands.shape[0], 3), device=self.device)
+        scale1[:, 0] = torch.abs(z_commands)
+
+        scale2 = torch.ones((self._commands.shape[0], 3), device=self.device)
+        scale2[:, 0] = torch.abs(robot_yaw)
+
+        offset1 = torch.zeros((self._commands.shape[0], 3), device=self.device)
+        offset1[:, 1] = 0
+
+        offset2 = torch.zeros((self._commands.shape[0], 3), device=self.device)
+        offset2[:, 1] = 0
+
+        marker_scales = torch.concat(
+            [torch.ones((3 * self._commands.shape[0], 3), device=self.device), scale1, scale2], dim=0
+        )
+
+        marker_locations = torch.concat(
+            [
+                robot_pos,
+                robot_pos + xy_commands,
+                robot_pos + self.robots["robot_0"].data.root_lin_vel_b,
+                robot_pos + offset1,
+                robot_pos + offset2,
+            ],
+            dim=0,
+        )
+
+        _90 = (-3.14 / 2) * torch.ones(self._commands.shape[0]).to(self.device)
+
+        marker_orientations = quat_from_angle_axis(
+            torch.concat(
+                [
+                    torch.zeros(3 * self._commands.shape[0]).to(self.device),
+                    torch.sign(z_commands) * _90,
+                    torch.sign(robot_yaw) * _90,
+                ],
+                dim=0,
+            ),
+            torch.tensor([0.0, 1.0, 0.0], device=self.device),
+        )
+
+        self.my_visualizer.visualize(
+            marker_locations, marker_orientations, scales=marker_scales, marker_indices=marker_ids
+        )
+
+    def _get_rewards(self) -> dict:
+        self._draw_markers(self._commands)
+        # linear velocity tracking
+        lin_vel_error = torch.sum(torch.square(self._commands[:, :2] - self.robots["robot_0"].data.root_lin_vel_b[:, :2]), dim=1)
+        lin_vel_error_mapped = torch.exp(-lin_vel_error / 0.25)
+        # yaw rate tracking
+        yaw_rate_error = torch.square(self._commands[:, 2] - self.robots["robot_0"].data.root_ang_vel_b[:, 2])
+        yaw_rate_error_mapped = torch.exp(-yaw_rate_error / 0.25)
+        # z velocity tracking
+        z_vel_error = torch.square(self.robots["robot_0"].data.root_lin_vel_b[:, 2])
+        # angular velocity x/y
+        ang_vel_error = torch.sum(torch.square(self.robots["robot_0"].data.root_ang_vel_b[:, :2]), dim=1)
+        # joint torques
+        joint_torques = torch.sum(torch.square(self.robots["robot_0"].data.applied_torque), dim=1)
+        # joint acceleration
+        joint_accel = torch.sum(torch.square(self.robots["robot_0"].data.joint_acc), dim=1)
+        # action rate
+        action_rate = torch.sum(torch.square(self.actions["robot_0"] - self.previous_actions["robot_0"]), dim=1)
+        # feet air time
+        first_contact = self.contact_sensors["robot_0"].compute_first_contact(self.step_dt)[:, self.feet_ids["robot_0"]]
+        last_air_time = self.contact_sensors["robot_0"].data.last_air_time[:, self.feet_ids["robot_0"]]
+        air_time = torch.sum((last_air_time - 0.5) * first_contact, dim=1) * (
+            torch.norm(self._commands[:, :2], dim=1) > 0.1
+        )
+        # undesired contacts
+        net_contact_forces = self.contact_sensors["robot_0"].data.net_forces_w_history
+        is_contact = (
+            torch.max(torch.norm(net_contact_forces[:, :, self.undesired_body_contact_ids["robot_0"]], dim=-1), dim=1)[0] > 1.0
+        )
+        contacts = torch.sum(is_contact, dim=1)
+        # flat orientation
+        flat_orientation = torch.sum(torch.square(self.robots["robot_0"].data.projected_gravity_b[:, :2]), dim=1)
+
+        rewards = {
+            "track_lin_vel_xy_exp": lin_vel_error_mapped * self.cfg.lin_vel_reward_scale * self.step_dt,
+            "track_ang_vel_z_exp": yaw_rate_error_mapped * self.cfg.yaw_rate_reward_scale * self.step_dt,
+            "lin_vel_z_l2": z_vel_error * self.cfg.z_vel_reward_scale * self.step_dt,
+            "ang_vel_xy_l2": ang_vel_error * self.cfg.ang_vel_reward_scale * self.step_dt,
+            "dof_torques_l2": joint_torques * self.cfg.joint_torque_reward_scale * self.step_dt,
+            "dof_acc_l2": joint_accel * self.cfg.joint_accel_reward_scale * self.step_dt,
+            "action_rate_l2": action_rate * self.cfg.action_rate_reward_scale * self.step_dt,
+            "feet_air_time": air_time * self.cfg.feet_air_time_reward_scale * self.step_dt,
+            "undesired_contacts": contacts * self.cfg.undesired_contact_reward_scale * self.step_dt,
+            "flat_orientation_l2": flat_orientation * self.cfg.flat_orientation_reward_scale * self.step_dt,
+        }
+        reward = torch.sum(torch.stack(list(rewards.values())), dim=0)
+        # Logging
+        for key, value in rewards.items():
+            self._episode_sums[key] += value
+        return {"robot_0":reward}
+
+    def _get_dones(self) -> tuple[dict, dict]:
+        time_out = self.episode_length_buf >= self.max_episode_length - 1
+        net_contact_forces = self.contact_sensors["robot_0"].data.net_forces_w_history
+        died = torch.any(torch.max(torch.norm(net_contact_forces[:, :, self.base_ids["robot_0"]], dim=-1), dim=1)[0] > 1.0, dim=1)
+        return {"robot_0": died}, {"robot_0": time_out}
+
+    def _reset_idx(self, env_ids: torch.Tensor | None):
+        if env_ids is None or len(env_ids) == self.num_envs:
+            env_ids = self.robots["robot_0"]._ALL_INDICES
+        self.robots["robot_0"].reset(env_ids)
+        super()._reset_idx(env_ids)
+        if len(env_ids) == self.num_envs:
+            # Spread out the resets to avoid spikes in training when many environments reset at a similar time
+            self.episode_length_buf[:] = torch.randint_like(self.episode_length_buf, high=int(self.max_episode_length))
+        self.actions["robot_0"][env_ids] = 0.0
+        self.previous_actions["robot_0"][env_ids] = 0.0
+        # Sample new commands
+        self._commands[env_ids] = torch.zeros_like(self._commands[env_ids]).uniform_(-1.0, 1.0)
+        # Reset robot state
+        joint_pos = self.robots["robot_0"].data.default_joint_pos[env_ids]
+        joint_vel = self.robots["robot_0"].data.default_joint_vel[env_ids]
+        default_root_state = self.robots["robot_0"].data.default_root_state[env_ids]
+        default_root_state[:, :3] += self._terrain.env_origins[env_ids]
+        self.robots["robot_0"].write_root_pose_to_sim(default_root_state[:, :7], env_ids)
+        self.robots["robot_0"].write_root_velocity_to_sim(default_root_state[:, 7:], env_ids)
+        self.robots["robot_0"].write_joint_state_to_sim(joint_pos, joint_vel, None, env_ids)
+        # Logging
+        extras = dict()
+        for key in self._episode_sums.keys():
+            episodic_sum_avg = torch.mean(self._episode_sums[key][env_ids])
+            extras["Episode_Reward/" + key] = episodic_sum_avg / self.max_episode_length_s
+            self._episode_sums[key][env_ids] = 0.0
+        self.extras["log"] = dict()
+        self.extras["log"].update(extras)
\ No newline at end of file

From 9960fb8410303347d02a36e7c35e8fb6a0bf5b6d Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 18:29:43 -0600
Subject: [PATCH 07/11] formatting

---
 CONTRIBUTORS.md                               |  2 +-
 .../direct/anymal_c/anymal_c_happo_env.py     | 28 +++++++++++++------
 2 files changed, 20 insertions(+), 10 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 487d8b15737..f17b71a4129 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -186,4 +186,4 @@ Guidelines for modifications:
 
 * Antonio Serrano-Muñoz
 * Ben Johnston
-* Clemens Schwarke
\ No newline at end of file
+* Clemens Schwarke
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
index fa05f0a4c60..05f3ced8009 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
@@ -1,3 +1,8 @@
+# Copyright (c) 2022-2025, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
+# All rights reserved.
+#
+# SPDX-License-Identifier: BSD-3-Clause
+
 # Copyright (c) 2022-2025, The Isaac Lab Project Developers.
 # All rights reserved.
 #
@@ -28,6 +33,7 @@
 ##
 from isaaclab_assets.robots.anymal import ANYMAL_C_CFG  # isort: skip
 
+
 @configclass
 class EventCfg:
     """Configuration for randomization."""
@@ -153,13 +159,10 @@ class AnymalCHappoEnvCfg(DirectMARLEnvCfg):
     flat_orientation_reward_scale = -5.0
 
 
-
 class AnymalCHappoEnv(DirectMARLEnv):
     cfg: AnymalCHappoEnvCfg
 
-    def __init__(
-        self, cfg: AnymalCHappoEnvCfg, render_mode: str | None = None, **kwargs
-    ):
+    def __init__(self, cfg: AnymalCHappoEnvCfg, render_mode: str | None = None, **kwargs):
         super().__init__(cfg, render_mode, **kwargs)
         # Joint position command (deviation from default joint positions)
 
@@ -330,7 +333,9 @@ def _draw_markers(self, command):
     def _get_rewards(self) -> dict:
         self._draw_markers(self._commands)
         # linear velocity tracking
-        lin_vel_error = torch.sum(torch.square(self._commands[:, :2] - self.robots["robot_0"].data.root_lin_vel_b[:, :2]), dim=1)
+        lin_vel_error = torch.sum(
+            torch.square(self._commands[:, :2] - self.robots["robot_0"].data.root_lin_vel_b[:, :2]), dim=1
+        )
         lin_vel_error_mapped = torch.exp(-lin_vel_error / 0.25)
         # yaw rate tracking
         yaw_rate_error = torch.square(self._commands[:, 2] - self.robots["robot_0"].data.root_ang_vel_b[:, 2])
@@ -354,7 +359,10 @@ def _get_rewards(self) -> dict:
         # undesired contacts
         net_contact_forces = self.contact_sensors["robot_0"].data.net_forces_w_history
         is_contact = (
-            torch.max(torch.norm(net_contact_forces[:, :, self.undesired_body_contact_ids["robot_0"]], dim=-1), dim=1)[0] > 1.0
+            torch.max(torch.norm(net_contact_forces[:, :, self.undesired_body_contact_ids["robot_0"]], dim=-1), dim=1)[
+                0
+            ]
+            > 1.0
         )
         contacts = torch.sum(is_contact, dim=1)
         # flat orientation
@@ -376,12 +384,14 @@ def _get_rewards(self) -> dict:
         # Logging
         for key, value in rewards.items():
             self._episode_sums[key] += value
-        return {"robot_0":reward}
+        return {"robot_0": reward}
 
     def _get_dones(self) -> tuple[dict, dict]:
         time_out = self.episode_length_buf >= self.max_episode_length - 1
         net_contact_forces = self.contact_sensors["robot_0"].data.net_forces_w_history
-        died = torch.any(torch.max(torch.norm(net_contact_forces[:, :, self.base_ids["robot_0"]], dim=-1), dim=1)[0] > 1.0, dim=1)
+        died = torch.any(
+            torch.max(torch.norm(net_contact_forces[:, :, self.base_ids["robot_0"]], dim=-1), dim=1)[0] > 1.0, dim=1
+        )
         return {"robot_0": died}, {"robot_0": time_out}
 
     def _reset_idx(self, env_ids: torch.Tensor | None):
@@ -411,4 +421,4 @@ def _reset_idx(self, env_ids: torch.Tensor | None):
             extras["Episode_Reward/" + key] = episodic_sum_avg / self.max_episode_length_s
             self._episode_sums[key][env_ids] = 0.0
         self.extras["log"] = dict()
-        self.extras["log"].update(extras)
\ No newline at end of file
+        self.extras["log"].update(extras)

From e1aa7caed379886e2fde73a153fa5f5e7897f011 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 19:16:35 -0600
Subject: [PATCH 08/11] fixed formatting for licensing and incorrect file
 descriptions, also fixed incorrect in-place tensor modification

---
 scripts/reinforcement_learning/harl/play.py            |  5 -----
 scripts/reinforcement_learning/harl/train.py           |  5 -----
 .../isaaclab_tasks/direct/anymal_c/__init__.py         |  2 +-
 .../direct/anymal_c/anymal_c_happo_env.py              |  5 -----
 .../direct/multi_agent_coordination/__init__.py        |  7 +------
 .../direct/multi_agent_coordination/agents/__init__.py |  5 -----
 .../anymal_c_multi_agent_bar_env.py                    |  5 -----
 .../multi_agent_coordination/h1_anymal_push_env.py     | 10 +---------
 8 files changed, 3 insertions(+), 41 deletions(-)

diff --git a/scripts/reinforcement_learning/harl/play.py b/scripts/reinforcement_learning/harl/play.py
index 7ec343fa0e5..65cbab9d52d 100644
--- a/scripts/reinforcement_learning/harl/play.py
+++ b/scripts/reinforcement_learning/harl/play.py
@@ -3,11 +3,6 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
 """Train an algorithm."""
 
 import argparse
diff --git a/scripts/reinforcement_learning/harl/train.py b/scripts/reinforcement_learning/harl/train.py
index f05199da3ee..ba4eb471de2 100644
--- a/scripts/reinforcement_learning/harl/train.py
+++ b/scripts/reinforcement_learning/harl/train.py
@@ -3,11 +3,6 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
 """Train an algorithm."""
 
 import argparse
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py
index b33463ba335..5cfb90ed6ed 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/__init__.py
@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 """
-Ant locomotion environment.
+Anymal-C environment.
 """
 
 import gymnasium as gym
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
index 05f3ced8009..5d9eb26c614 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/anymal_c/anymal_c_happo_env.py
@@ -3,11 +3,6 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
 from __future__ import annotations
 
 import copy
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
index dfc32637c1a..1a072ce5318 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/__init__.py
@@ -3,13 +3,8 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
 """
-Ant locomotion environment.
+Multi Agent Envs.
 """
 
 import gymnasium as gym
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
index 2227acce701..2e924fbf1b1 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/agents/__init__.py
@@ -2,8 +2,3 @@
 # All rights reserved.
 #
 # SPDX-License-Identifier: BSD-3-Clause
-
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
index 6216f0d7e17..c7e5473bcdb 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/anymal_c_multi_agent_bar_env.py
@@ -3,11 +3,6 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
 from __future__ import annotations
 
 import copy
diff --git a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
index 16f61c689f6..4628868467b 100644
--- a/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
+++ b/source/isaaclab_tasks/isaaclab_tasks/direct/multi_agent_coordination/h1_anymal_push_env.py
@@ -3,11 +3,6 @@
 #
 # SPDX-License-Identifier: BSD-3-Clause
 
-# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
-# All rights reserved.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-
 from __future__ import annotations
 
 import copy
@@ -508,10 +503,7 @@ def _draw_markers(self, command):
             [torch.ones((3 * self._commands.shape[0], 3), device=self.device), scale1, scale2], dim=0
         )
 
-        # obj_vel = self.object.data.root_com_lin_vel_b.clone()
-        # obj_vel[:, 2] = 0
-
-        obj_vel = self.object.data.root_com_lin_vel_b
+        obj_vel = self.object.data.root_com_lin_vel_b.clone()
         obj_vel[:, 2] = 0
 
         offset = torch.tensor([0, 2, 0], device=self.device)

From 2409ce32461f2ccbcb2e8ab3119eca53e8a1943f Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 19:19:55 -0600
Subject: [PATCH 09/11] sorted new contributors alphabetically

---
 CONTRIBUTORS.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index f17b71a4129..91e256bcaf2 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -56,6 +56,7 @@ Guidelines for modifications:
 * Cathy Y. Li
 * Cheng-Rong Lai
 * Chenyu Yang
+* Christopher Allred
 * Connor Smith
 * CY (Chien-Ying) Chen
 * David Yang
@@ -73,8 +74,10 @@ Guidelines for modifications:
 * Hongwei Xiong
 * Hongyu Li
 * Huihua Zhao
+* Isaac Peterson
 * Iretiayo Akinola
 * Jack Zeng
+* Jacob Haight
 * Jan Kerner
 * Jean Tampon
 * Jeonghwan Kim
@@ -152,9 +155,6 @@ Guidelines for modifications:
 * Zoe McCarthy
 * David Leon
 * Song Yi
-* Jacob Haight
-* Isaac Peterson
-* Christopher Allred
 
 ## Acknowledgements
 

From d46ffc5b215291880c000a54782162af9b499bb7 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Wed, 29 Oct 2025 19:28:09 -0600
Subject: [PATCH 10/11] removed accidentally copied content to the end of the
 contributors file

---
 CONTRIBUTORS.md | 23 +----------------------
 1 file changed, 1 insertion(+), 22 deletions(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 91e256bcaf2..1c3e73f7c31 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -165,25 +165,4 @@ Guidelines for modifications:
 * Hammad Mazhar
 * Marco Hutter
 * Yan Chang
-* Yashraj Narang
-
-To see the full list of contributors, please check the revision history in the source control.
-
-Guidelines for modifications:
-
-* Please keep the **lists sorted alphabetically**.
-* Names should be added to this file as: *individual names* or *organizations*.
-* E-mail addresses are tracked elsewhere to avoid spam.
-
-## Developers
-
-* Boston Dynamics AI Institute, Inc.
-* ETH Zurich
-* NVIDIA Corporation & Affiliates
-* University of Toronto
-
----
-
-* Antonio Serrano-Muñoz
-* Ben Johnston
-* Clemens Schwarke
+* Yashraj Narang
\ No newline at end of file

From 00a4b383baf4eb1edbc9b99cbe38f3daef3e3e30 Mon Sep 17 00:00:00 2001
From: Isaac Peterson <isaac.peterson@usu.edu>
Date: Thu, 30 Oct 2025 16:27:29 -0600
Subject: [PATCH 11/11] fixed author name in contributors

---
 CONTRIBUTORS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index 1c3e73f7c31..592a10c9497 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -77,7 +77,7 @@ Guidelines for modifications:
 * Isaac Peterson
 * Iretiayo Akinola
 * Jack Zeng
-* Jacob Haight
+* Jacob Hate
 * Jan Kerner
 * Jean Tampon
 * Jeonghwan Kim