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NeurIPS 2026TsinghuaU Hong KongHong KongXPeng MotorsTencent AI LabVision-language-action models

UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling

UniT unifies human and humanoid actions via visual anchoring into shared latent tokens for scalable policy learning and world modeling. It achieves state-of-the-art data efficiency, zero-shot transfer, and cross-embodiment dynamics alignment.

Boyu Chen, Yi Chen, Lu QIU, Jerry Bai and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 32 on Hugging Face · Code ★ 103

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NeurIPS 2026XPeng MotorsSouthern University of Science aFudanThe Hong Kong PolytechnicAlibaba GroupAgent benchmarks & environments

Done, But Not Sure: Disentangling World Completion from Self-Termination in Embodied Agents

VIGIL decouples world-state completion from terminal commitment in embodied agents, revealing that comparable execution yields up to 19.7 pp differences in correct episode termination.

Ying Chen, Lihuang Fang, Rui Jiang, Mingxu Wang and 3 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 3/5