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wertyuilife 81fea30614 update installation guide in readme.

2026-04-17 22:00:42 +08:00

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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.

Installation Guide

1. Install IsaacLab

Install IsaacLab 2.3.0 release by following the installation guide. Please make sure to clone the IsaacLab repository and perform the installation using the release/2.3.0 branch.

After installation, your environment should satisfies:

isaacsim <= 5.1.0.0   # tested on 5.1.0.0
isaaclab <= 0.54.3    # tested on 0.54.3, 0.53.1
isaaclab-rl <= 0.4.7  # tested on 0.4.7, 0.4.4

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. Install them in editable mode:

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

3. Install MuJoCo (Optional, for Sim2Sim)

To enable MuJoCo-based simulation:

pip install mujoco  # tested on 3.4.0 and 3.6.0

Training and Evaluation

Run the following commands:

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

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

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:

Environment configuration

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

RL algorithm configuration

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

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:

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

MuJoCo Sim2Sim

Set the policy_path in deploy/deploy_mujoco/configs/go2.yaml:

policy_path: "{ROOT_DIR}/deploy/pre_train/go2/xxx.pt" # policy.pt exported by running scripts/rsl_rl/play.py

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)

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"

Differences from `go2_rl_gym`

Different terrain composition
Different tracking reward formulation (fixed sigma vs. dynamic sigma)
Lack domain_rand: randomize_motor_strength

TODO

Try replacing ActionManager-level delay with DelayedPDActuatorCfg or UnitreeActuatorCfg_Go2HV, and make sure the randomization of motor parameters works correctly.

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

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