Geometrically faithful and functional articulated 3D assets are essential for real-to-sim robot manipulation, where policies trained in simulation must transfer to physical objects. Recent mesh-based methods learn to infer articulation from annotated 3D assets, but deployment remains challenging when real-world objects...
Chuan-Rui Zhang, Zai-Jia Yang, Duo-Min Wang et al.· 0 citations
Online reinforcement learning (RL) enables robot policies to improve through physical interaction, but the assistance they require changes as their competence evolves. Existing intervention strategies based on offline estimates or fixed decision rules can therefore become mismatched to the current policy. To address th...
Yu-Dong Lin, Hao-Yuan Deng, Zhuo-Xuan Yuan et al.· 0 citations
Vision-Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify continuous, temporally correlated actions. We introduce CertVLA, a certified defense for closed-loop VLA control under bounded patch and text...
Hui Lu, Zhi-Jie Peng, Yuqi Lin et al.· 0 citations
Facet-0 unifies multimodal representation learning and reinforcement learning post-training around a joint action-wrench proposal, which reaches 82% mean success on five sub-millimeter computer-assembly tasks, compared with 15% for the strongest baseline.
Hao-Yuan Deng, Hai-Chao Liu, Wen-Kai Guo et al.· 1 citation
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