BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning

[arXiv] [Paper] [Website]

Code

Code will be released in stages:

• [x] Pretrained checkpoints + sim-to-sim / sim-to-real deployment
  → deploy branch

• [x] Minimal inference code + tutorial
  → minimal_inference branch

• [x] Full training and evaluation pipelines

• [x] A distributed training in MJLab (6 hours training) + Teleoperation
  → UFO implementation

• [ ] Minimal training code (RTX 4090 support)

BFM-Zero Training

Humanoidverse training for BFM-Zero with Isaac Sim or MuJoCo.

Requirements

• Python 3.10
• CUDA-capable GPU
• Isaac Sim (Linux) or MuJoCo for simulation

Installation

1. Clone and fetch large files (Git LFS)

This repo uses Git LFS for motion data and model. After cloning, install LFS and pull the large files:

git clone https://github.com/LeCAR-Lab/BFM-Zero.git
cd BFM-Zero
git lfs install
git lfs pull

Note: If the repository exceeded its LFS budget,  you can access the data here: https://huggingface.co/LeCAR-Lab/BFM-Zero/tree/main/data

2. Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

Or with pip: pip install uv

3. Install dependencies

From this directory (BFM-Zero):

uv sync

Data

• Motion data: Included via Git LFS in humanoidverse/data/ after git lfs pull. lafan_29dof.pkl is for evaluation; lafan_29dof_10s-clipped.pkl is for training.
• If you are unsure about the data format, please check the discussion in Issue #12.

Training

Launch

uv run python -m humanoidverse.train

Note: change buffer_device to "cuda:0" if you have larger vRAM.

Main parameters

Training is driven by humanoidverse.train (see train_bfm_zero() in train.py). Key options:

Override from code by passing a custom TrainConfig, or extend the CLI to accept Hydra/tyro overrides.

Tips: After 50-100 M steps training, eval/emd should lower than 0.75.

Inference

After training, three scripts handle inference and export:

Example outputs (videos)

Videos from BFM-Zero/model after running each inference script with --save_mp4:

1. Tracking inference — expert (left) vs policy (right):

2. Goal inference — goal-reaching rollout:

3. Reward inference — example task (e.g. move-ego):

All scripts use tyro for the CLI. General usage:

uv run python -m humanoidverse.tracking_inference --help
uv run python -m humanoidverse.goal_inference --help
uv run python -m humanoidverse.reward_inference --help

Common arguments:

• --model_folder: Path to the trained model directory (must contain checkpoint/ and config.json).
• --data_path (optional): Override the default LaFan data path.
• --simulator: isaacsim (default) or mujoco. Use --simulator mujoco to run without Isaac Lab (MuJoCo only; output is directly usable for sim2sim visualization).
• --headless (default: True): Run without GUI; use --no-headless to show the viewer.
• --save_mp4: Save rendered videos.

Output: All inference scripts export the policy to ONNX ({model_name}.onnx) in their respective output subdirectories under exported/.

Tracking inference

Runs motion tracking, exports ONNX, and optionally saves a comparison video (expert vs policy).

uv run python -m humanoidverse.tracking_inference \
    --model_folder /path/to/model \
    --data_path humanoidverse/data/lafan_29dof.pkl \
    --no-headless \
    --save_mp4

• --model_folder should point to the outer model directory (the one that contains checkpoint/).
• You can set motion IDs and visualization length inside the script if exposed.

Outputs (under model_folder/tracking_inference/):

• zs_{MOTION_ID}.pkl: Latent (z) for each motion.
• tracking.mp4: Expert vs policy comparison (when --save_mp4 is set).

Goal inference

Computes (z) for predefined goals and optionally renders goal-reaching videos.

uv run python -m humanoidverse.goal_inference \
    --model_folder /path/to/model \
    --data_path humanoidverse/data/lafan_29dof.pkl \
    --save_mp4

• Iterates over predefined goals and computes the corresponding (z).
• Requires goal_frames_lafan29dof.json (the script searches for it in several locations).

Outputs (under model_folder/goal_inference/):

• goal_reaching.pkl: Dictionary {goal_name -> z}.
• videos/*.mp4: Per-goal videos (if --save_mp4 True).

Reward inference

Runs reward-based task inference: computes (z) and optionally runs rollouts for evaluation.

uv run python -m humanoidverse.reward_inference \
    --model_folder /path/to/model \
    --save_mp4 

Key arguments:

Outputs (under model_folder/reward_inference/):

• reward_locomotion.pkl: Dictionary {task_name -> z}.
• videos/*.mp4: Per-task videos (when --save_mp4 is set).

License

BFM-Zero is licensed under the CC BY-NC 4.0 license. See LICENSE for details.

Citation

If you find this project useful in your research, please consider citing:

@misc{li2025bfmzeropromptablebehavioralfoundation,
      title={BFM-Zero: A Promptable Behavioral Foundation Model for Humanoid Control Using Unsupervised Reinforcement Learning}, 
      author={Yitang Li and Zhengyi Luo and Tonghe Zhang and Cunxi Dai and Anssi Kanervisto and Andrea Tirinzoni and Haoyang Weng and Kris Kitani and Mateusz Guzek and Ahmed Touati and Alessandro Lazaric and Matteo Pirotta and Guanya Shi},
      year={2025},
      eprint={2511.04131},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2511.04131}, 
}

Contact

If you have any problems, please contact liyitang475@gmail.com.