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.

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.

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.

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.