arXiv (All)
open access
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 v
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Humanoid World Action Model With Joint State--Action Generation#1249451
- X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control#1109258
- WholeBodyWAM: Generalizing Pre-trained World-Action Priors to Humanoid Loco-Manipulation via WBC-Grounded Coordination#1122658
- Complementary Future Token Pruning for World Action Models#1356172
- Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors#1326806
Record · ID 1326247
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive