μ₀: A Scalable 3D Interaction-Trace World Model

μ₀ is a trace world model that operates in 3D space rather than pixel or action space. Given an image, a language instruction, and a short history of keypoints, μ₀ predicts the future 3D traces of semantic interaction points — objects, tools, hands, and contact regions — i.e. what must move to accomplish a task. Because these traces are embodiment-agnostic, μ₀ can be pretrained from video alone and transferred across robot embodiments.

🌐 Project page: https://mu0-wm.github.io
📄 Paper: arXiv:2606.13769
💻 Code: https://github.com/Yoonkyo/mu0

This repository hosts the release artifacts for μ₀ — the trained world-model checkpoint, normalization stats, and the evaluation test set. The downstream policy checkpoints live at furonghuang-lab/mu0-policy.

Path	Description
`final_ckpt/`	Released world-model checkpoint (`config.json`, `meta.json`, `model.safetensors`)
`normalizer_stats.json`	Delta/depth normalization stats used in training and evaluation
`test_set.tar`	Evaluation episodes (extracts to `test_set/`)

Download

pip install -U "huggingface_hub[hf_transfer]"
hf download furonghuang-lab/mu0 --local-dir mu0_release
cd mu0_release && tar -xf test_set.tar    # only needed for evaluation

You can also fetch individual files via their resolve URLs, e.g. https://huggingface.co/furonghuang-lab/mu0/resolve/main/final_ckpt/model.safetensors.

See docs/release/EVALUATION.md in the code repo for usage.

Citation

@article{lee2026mu0,
  title={$\mu_0$: A Scalable 3D Interaction-Trace World Model},
  author={Lee, Seungjae and Jung, Yoonkyo and Lee, Jusuk and Shin, Jonghun and
          Shahidzadeh, Amir Hossein and Lee, Yao-Chih and Kim, H. Jin and
          Huang, Jia-Bin and Huang, Furong},
  journal={arXiv preprint arXiv:2606.13769},
  year={2026}
}

Acknowledgements

μ₀'s training, dataset, and policy infrastructure are built on LeRobot (Apache-2.0) by Hugging Face. If you use these artifacts, please also cite LeRobot.