go2_rl_robotlab

Overview

Train Unitree Go2 with MoE-CTS on IsaacLab and deploy it to MuJoCo.

This is a reproduction version of go2_rl_gym on RobotLab/IsaacLab.

Installation Guide

1. Install IsaacLab

Install IsaacLab 2.3.0 release by following the installation guide.

After installation, your environment should satisfy the following requirements:

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.

2. Install customized RSL-RL and RobotLab

We uses a customized version of rsl_rl and robot_lab. To install it, run the following commands:

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

3. Install MuJoCo for Sim2Sim (optional)

If you want to use mujoco for Sim2Sim, install it by running the following command:

pip install mujoco # tested on mujoco 3.4.0 & 3.6.0

Train and Play

Use the following commands to train and play:

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

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

Configuration

1. Modify source/robot_lab/robot_lab/tasks/go2/env_cfg.py for environment config.

2. Modify source/robot_lab/robot_lab/tasks/go2/rsl_rl_cfg.py for algorithm config.

3. Modify source/robot_lab/robot_lab/tasks/go2/__init__.py to add your own task with new config.

4. Add args in commands to override above configs, for example:

   --experiment_name=moe_cts
   --run_name=v1
   --num_envs=16384
   --resume
   --checkpoint=path/to/your/checkpoint
   

   for more usage, see robot_lab.

MuJoCo Sim2Sim

Use the following command to run the Sim2Sim with MuJoCo:

python deploy/deploy_mujoco/deploy_go2.py

• Automatic detection: When connecting the handle, the script automatically activates the handle control mode.
• Controller not available: The script will use the default commands in the configuration file.

Handle axis mapping:

• LX/LY: Forward/Lateral Speed Command
• RX: Angular velocity (steering) command

Modify the xml_path parameter in deploy/deploy_mujoco/config/go2.yaml to switch simulation scenarios:

# Flat
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}/resource/go2/your-custom-scene.xml"

Differences with go2_rl_gym

• Terrain's composition are different(see code).
• tracking reward are different (fixed sigma vs. dynamic sigma).

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