Author

Ming-Zhi Pei

2 papers indexed here

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Preprint Sep 2026

Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction

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

#artificial intelligence Preprint Sep 2026

Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions

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