Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. Reinforcement learning (RL) offers an effective avenue for further improving VLA policies. However, existing VLA-RL methods typically suffer from delay...
Li-Kui Zhang, Zhe Zhu, Zhi-Hao Zhan et al.· 0 citations
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
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
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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