go2_rl_robotlab

Overview

Trains the Unitree Go2 robot using MoE-CTS in IsaacLab and deploys the trained policy to MuJoCo for simulation transfer (Sim2Sim).

It is a reproduction of go2_rl_gym, adapted to the RobotLab / IsaacLab ecosystem.

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Installation Guide

1. Install IsaacLab

Install IsaacLab 2.3.0 release by following the installation guide.

After installation, your environment should satisfies:

isaacsim <= 5.1.0.0   # tested on 5.1.0.0
isaaclab <= 0.53.1    # tested on 0.53.1
isaaclab-rl <= 0.4.7  # tested on 0.4.7

Higher version may cause conflicts with our customized rsl_rl==3.3.0 and robot_lab==2.3.0.

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2. Install Customized RSL-RL and RobotLab

We uses a customized version of rsl_rl and robot_lab. Install them in editable mode:

python -m pip install -e source/robot_lab
python -m pip install -e source/rsl_rl

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3. Install MuJoCo (Optional, for Sim2Sim)

To enable MuJoCo-based simulation:

pip install mujoco  # tested on 3.4.0 and 3.6.0

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Training and Evaluation

Run the following commands:

# Train
python scripts/rsl_rl/train.py \
    --task=RobotLab-Go2-v0 \
    --headless

# Play / Evaluate
python scripts/rsl_rl/play.py \
    --task=RobotLab-Go2-v0

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Configuration

The training pipeline can be configured at two levels: task-level Python configuration files and runtime arguments passed to the training script.

Task-Level Configuration

The default task settings are defined in the following files:

1. Environment configuration

   source/robot_lab/robot_lab/tasks/go2/env_cfg.py
   

2. RL algorithm configuration

   source/robot_lab/robot_lab/tasks/go2/rsl_rl_cfg.py
   

3. Task registration

   source/robot_lab/robot_lab/tasks/go2/__init__.py
   

Runtime Overrides

In addition to the default configuration files, train.py and play.py supports several command-line arguments for runtime overrides:

--experiment_name <YOUR_EXP_NAME>
--run_name <YOUR_RUN_NAME>
--num_envs <NUM_ENVS>
--checkpoint <PATH_TO_CHECKPOINT>

For more details, refer to the robot_lab repo.

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MuJoCo Sim2Sim

Run the deployment script:

python deploy/deploy_mujoco/deploy_go2.py

Controller Behavior

• Automatic detection: If a controller is connected, control mode is enabled automatically.
• Fallback mode: If no controller is detected, default commands from the config file are used.

Controller Mapping

Input	Function
LX / LY	Forward / lateral velocity
RX	Angular velocity (steering)

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Switching Simulation Scenarios

Modify xml_path in:

deploy/deploy_mujoco/config/go2.yaml

# Flat terrain
xml_path: "{ROOT_DIR}/resources/go2/flat.xml"

# Stairs
xml_path: "{ROOT_DIR}/resources/go2/stairs.xml"

# Boxes
xml_path: "{ROOT_DIR}/resources/go2/boxes.xml"

# Custom
xml_path: "{ROOT_DIR}/resources/go2/your-custom-scene.xml"

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Differences from go2_rl_gym

• Different terrain composition
• Different tracking reward formulation (fixed sigma vs. dynamic sigma)

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TODO

• Try replacing ActionManager-level delay with DelayedPDActuatorCfg

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Acknowledgements

This repository would not exist without the following open-source projects:

• isaac_lab: Unified framework for robot learning built on NVIDIA Isaac Sim.
• rsl_rl: Reinforcement learning algorithms.
• robot_lab: RL Extension Library for Robots, Based on IsaacLab.
• mujoco: High-performance CPU physics simulator.

Related publications implemented in this repo:

• CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion