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

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

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 collection? We present DEX-X, a framework for learning visual-tactile dexterous manipulation from human videos through simulation. Our key insight is that simulation can serve as a tactile completion engine. Given monocular human demonstrations, DEX-X reconstructs hand-object int

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