OpenTrack

GALBOT · Tsinghua

📃Paper | 🏠Website

This repository is the official implementation of OpenTrack, an open-source humanoid motion tracking codebase that uses MuJoCo for simulation and supports multi-GPU parallel training.

News 🚩

[November 30, 2025] LAFAN1 generalist v1 released. Now you can track cartwheel, kungfu, fall and getup, and many other motions within a single policy.

[September 19, 2025] Simple Domain Randomization released.

[September 19, 2025] Tracking codebase released.

TODOs

• Release motion tracking codebase
• Release simple domain randomization
• Release pretrained LAFAN1 generalist v1 checkpoints
• Release DAgger code
• Release AnyAdapter
• Release more pretrained checkpoints
• Release real-world deployment code

Prepare

1. Clone the repository:

   git clone git@github.com:GalaxyGeneralRobotics/OpenTrack.git

2. Create a virtual environment and install dependencies:

   uv sync -i https://pypi.org/simple 

3. Create a .env file in the project directory with the following content:

   export GLI_PATH=<your_root_path>/OpenTrack
   export WANDB_PROJECT=<your_project_name>
   export WANDB_ENTITY=<your_entity_name>
   export WANDB_API_KEY=<your_wandb_api_key>

4. Download the mocap data and put them under data/mocap/. Thanks for the retargeting motions of LAFAN1 dataset from LocoMuJoCo!

   The file structure should be like:

   data/
   |-- xmls
      |- ...
   |-- mocap
      |-- lafan1
         |-- UnitreeG1
               |-- dance1_subject1.npz
               |--- ...
   

Usage

Initialize environment

1. Initialize the MuJoCo environment:

   source .venv/bin/activate; source .env;

Play pretrained checkpoints

1. Download pretrained checkpoints and configs from checkpoints and configs, and put them under experiments/. Visualization results: videos.

2. Run the evaluation script:

   # your_exp_name=<timestamp>_<exp_name>
   python play_policy.py --exp_name <your_exp_name> [--use_viewer] [--use_renderer] [---play_ref_motion]

As of November 30, 2025, we have open-sourced a generalist model on LAFAN1, daggered from four teachers. This checkpoint was trained with simple domain randomization (DR). You may try deploying it on a Unitree G1 robot using your own deployment code, since we have not yet open-sourced our real-robot deployment pipeline.

Train from scratch

1. Train the model:

   # Train on a flat terrain:
   python train_policy.py --exp_name flat_terrain --terrain_type flat_terrain
   # Train on a rough terrain:
   python generate_terrain.py # generate various hfield with Perlin noise
   python train_policy.py --exp_name rough_terrain --terrain_type rough_terrain
   
   # For debug mode (quick testing training without logging)
   python train_policy.py --exp_name debug 

2. Evaluate the model First, convert the Brax model checkpoint to PyTorch:

   # your_exp_name=<timestamp>_<exp_name>
   python brax2torch.py --exp_name <your_exp_name>

   Next, run the evaluation script:

   # your_exp_name=<timestamp>_<exp_name>
   python play_policy.py --exp_name <your_exp_name> [--use_viewer] [--use_renderer] [---play_ref_motion]

Acknowledgement

This repository is build upon jax, brax, loco-mujoco, and mujoco_playground.

If you find this repository helpful, please cite our work:

@article{zhang2025track,
  title={Track any motions under any disturbances},
  author={Zhang, Zhikai and Guo, Jun and Chen, Chao and Wang, Jilong and Lin, Chenghuai and Lian, Yunrui and Xue, Han and Wang, Zhenrong and Liu, Maoqi and Lyu, Jiangran and others},
  journal={arXiv preprint arXiv:2509.13833},
  year={2025}
}