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Go2 Mjlab Princeton

The repo is forked from Unitree's RL Mjlab.

Installation

Follow the official installation steps below to install.

Nature's parkour

The parkour demo is added in as an additional task you can use to train Go2 with vision.

Training:

python scripts/train.py Unitree-Go2-Parkour --env.scene.num-envs=256

Tested on RTX4070 12GB. Takes 4 hours for 15,000 iterations.

Replay:

python3 scripts/play.py Unitree-Go2-Parkour --checkpoint-file=<path_to_model.pt>

Unitree RL Mjlab

✳️ Overview

Unitree RL Mjlab is a reinforcement learning project built upon the mjlab, using MuJoCo as its physics simulation backend, currently supporting Unitree Go2, A2, G1, H1_2 and R1.

Mjlab combines Isaac Lab's proven API with best-in-class MuJoCo physics to provide lightweight, modular abstractions for RL robotics research and sim-to-real deployment.

MuJoCo
Physical

📦 Installation and Configuration

Please refer to setup.md for installation and configuration steps.

🔁 Process Overview

The basic workflow for using reinforcement learning to achieve motion control is:

Train → Play → Sim2Real

  • Train: The agent interacts with the MuJoCo simulation and optimizes policies through reward maximization.
  • Play: Replay trained policies to verify expected behavior.
  • Sim2Real: Deploy trained policies to physical Unitree robots for real-world execution.

🛠️ Usage Guide

1. Velocity Tracking Training

Run the following command to train a velocity tracking policy:

python scripts/train.py Unitree-G1-Flat --env.scene.num-envs=4096

Multi-GPU Training: Scale to multiple GPUs using --gpu-ids:

python scripts/train.py Unitree-G1-Flat \
  --gpu-ids 0 1 \
  --env.scene.num-envs=4096
  • The first argument (e.g., Mjlab-Velocity-Flat-Unitree-G1) specifies the training task. Available velocity tracking tasks:
    • Unitree-Go2-Flat
    • Unitree-G1-Flat
    • Unitree-G1-23Dof-Flat
    • Unitree-H1_2-Flat
    • Unitree-A2-Flat
    • Unitree-R1-Flat

Note

For more details, refer to the mjlab documentation: mjlab documentation.

2. Motion Imitation Training

Train a Unitree G1 to mimic reference motion sequences.

2.1 Prepare Motion Files

Prepare csv motion files in mjlab/motions/g1/ and convert them to npz format:

python scripts/csv_to_npz.py \
--input-file src/assets/motions/g1/dance1_subject2.csv \
--output-name dance1_subject2.npz \
--input-fps 30 \
--output-fps 50

npz files will be stored at:：src/motions/g1/...

2.2 Training

After generating the NPZ file, launch imitation training:

python scripts/train.py Unitree-G1-Tracking --motion_file=src/assets/motions/g1/dance1_subject2.npz --env.scene.num-envs=4096

Note

For detailed motion imitation instructions, refer to the BeyondMimic documentation: BeyondMimic documentation.

⚙️ Parameter Description

  • --env.scene: simulation scene configuration (e.g., num_envs, dt, ground type, gravity, disturbances)
  • --env.observations: observation space configuration (e.g., joint state, IMU, commands, etc.)
  • --env.rewards: reward terms used for policy optimization
  • --env.commands: task commands (e.g., velocity, pose, or motion targets)
  • --env.terminations: termination conditions for each episode
  • --agent.seed: random seed for reproducibility
  • --agent.resume: resume from the last saved checkpoint when enabled
  • --agent.policy: policy network architecture configuration
  • --agent.algorithm: reinforcement learning algorithm configuration (PPO, hyperparameters, etc.)

Training results are stored at：logs/rsl_rl/<robot>_(velocity | tracking)/<date_time>/model_<iteration>.pt

3. Simulation Validation

To visualize policy behavior in MuJoCo:

Velocity tracking:

python scripts/play.py Unitree-G1-Flat --checkpoint_file=logs/rsl_rl/g1_velocity/2026-xx-xx_xx-xx-xx/model_xx.pt

Motion imitation:

python scripts/play.py Unitree-G1-Tracking --motion_file=src/assets/motions/g1/dance1_subject2.npz --checkpoint_file=logs/rsl_rl/g1_tracking/2026-xx-xx_xx-xx-xx/model_xx.pt

Note：

  • During training, policy.onnx and policy.onnx.data are also exported for deployment onto physical robots.

Visualization：

Go2 G1 H1_2 G1_mimic
go2 g1 h1_2 g1_mimic

4. Real Deployment

Before deployment, install the required communication tools:

4.1 Power On the Robot

Start the robot in suspended state and wait until it enters zero-torque mode.

4.2 Enable Debug Mode

While in zero-torque mode, press L2 + R2 on the controller. The robot will enter debug mode with joint damping enabled.

4.3 Connect to the Robot

Connect your PC to the robot via Ethernet. Configure the network as:

  • Address：192.168.123.222
  • Netmask：255.255.255.0

Use ifconfig to determine the Ethernet device name for deployment.

4.4 Compilation

Example: Unitree G1 velocity control. Place policy.onnx and policy.onnx.data into: deploy/robots/g1/config/policy/velocity/v0/exported. Then compile:

cd deploy/robots/g1
mkdir build && cd build
cmake .. && make

4.5 Deployment

After Compilation, run：

cd deploy/robots/g1/build
./g1_ctrl --network=enp5s0

Arguments：

  • network: Ethernet interface name (e.g., enp5s0)

Deployment Results：

Go2 G1 H1_2 G1_mimic

🎉 Acknowledgements

This project would not be possible without the contributions of the following repositories:

  • mjlab: training and execution framework
  • whole_body_tracking: versatile humanoid motion tracking framework
  • rsl_rl: reinforcement learning algorithm implementation
  • mujoco_warp: GPU-accelerated rendering and simulation interface
  • mujoco: high-fidelity rigid-body physics engine