UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross-embodiment chasm remains a fundamental challenge due to kinematic mismatches. We introduce UniT (Unified Latent Action Tokenizer via Visual Anchoring), a framework that establishes a unified physical language for human-to-humanoid transfer. Grounded in the philosophy that heterogeneous kinematics share universal visual consequences, UniT employs a tri-branch cross-reconstruction mechanism: actions predict vision to anchor kinematics to physical outcomes, while vision reconstructs actions to filter out irrelevant visual co...
| Authors | Boyu Chen, Yi Chen, Lu Qiu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 April 2026 |
| Last updated at source | 11 October 2026 |
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