Human videos are an abundant source of dexterous manipulation behaviors, but they lack tactile information that is crucial for contact-rich interaction. This raises a fundamental question: can robots learn deployable visual-tactile dexterous manipulation policies from human video demonstrations without robot-side data...
Ruo-Qu Chen, Fei-Xiang Ruan, Liu Cao et al.· 5 citations
Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to characterize physical interaction, especially when the contact region is occluded. Unlike sparse force or torque measurements at predefined regions, distributed tactile...
Zi-Hao Wang, Shu-Tong Liu, Si-Qi Zheng et al.· 0 citations
Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstrations is costly. A practical self-improving system must decide both what to teach next and where to apply that supervision. We present ROBOCOACH, a world-model-guided coachin...
Jia-Jun Liu, Yi-Fan Chen, Yi-Chao Liu et al.· 0 citations
CATok is proposed, a causal action tokenizer that reframes tokenization as a causally structured generative process, establishing a high-performance, scalable foundation for purely autoregressive VLA systems.
Chen-Yu Zhang, Yu-Hang Cao, Da-Ru Du et al.· 0 citations
Weave is presented, a unified framework for learning whole-body dexterous humanoid-object interaction from captured human demonstrations that converts captured human-object interactions into executable robot-object references through contact-aware retargeting and approach-motion completion.
Liu Cao, Xing-Ze Wu, Jing-Zhi Cui et al.· 2 citations
Experiments in both simulation and real-world settings demonstrate that R2S-Eval produces reliable and stable policy conclusions, achieves agreement with human preferences, substantially reduces repeated hardware-operation effort, and reveals behavior-quality differences that are not captured by binary success labels.
Yi-Di Wang, Fei-Xiang Ruan, Ruo-Qu Chen et al.· 2 citations
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