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Author

Tian-Le Zhang

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Preprint Sep 2026

ReF-HIL: Shaping the Critic around Human Action Neighborhoods for Efficient Human-in-the-Loop Reinforcement Learning

Human-in-the-loop reinforcement learning (HIL-RL) offers a promising route to efficient training of robotic manipulation policies by combining autonomous learning with human demonstrations and online corrections. However, insufficient use of successful human experience in value learning prolongs costly real-world train...

Shao-Yin Luo, Song Wang, Shi-Bo Xia et al. · 0 citations
Preprint Aug 2026

CometVLA: Co-Training on an Embodied Data Pyramid towards Physical Understanding

Results demonstrate that physical understanding pre-training genuinely benefits downstream manipulation inVision-language-action models, and introduce GAP tokens, a compact learnable bottleneck that isolates task-agnostic motion regularities and lets the action head consume physical commonsense without corrupting the p...

Hanwen Wan, Dafeng Chi, Lin-Bo Zhai et al. · 1 citation
Preprint Aug 2026

WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation

This work formalizes Observable Simulator Contract, a minimal contract that any action-conditioned physical simulator should satisfy: supplied actions must induce corresponding agent motion, and environment responses must be grounded in that realized motion.

P. Co, Sichen Hu, Chun-Xuan Jiao et al. · 1 citation

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