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

Ruoqu Chen; Feixiang Ruan; Liu Cao; Zihao Wang; Botian Xu; Shiqin Tong; Jiajun Liu; Mingzhi Pei; Chenyu Zhang; Wanli Xing; Kaifeng Zhang; Mengdi Xu

arXiv · 2609.07747

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

Ruoqu Chen·Feixiang Ruan·Liu Cao·Zihao Wang·Botian Xu·Shiqin Tong·Jiajun Liu·Mingzhi Pei·Chenyu Zhang·Wanli Xing·Kaifeng Zhang·Mengdi Xu

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Abstract

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 interactions in simulation, where physically grounded contact dynamics provide tactile supervision unavailable in the original videos. Leveraging this recovered tactile information, we train visual-tactile dexterous manipulation policies and distill them into deployable policies operating on point-cloud observations and tactile sensing. We demonstrate zero-shot sim-to-real transfer on a dexterous hand-arm platform across diverse grasping and contact-rich tool-use tasks. The teacher policy achieves 65.9% average success across six task categories in simulation, while the distilled visual-tactile policy achieves 93% success on real-world cube picking and 53% on the challenging table-cleaning task. Zero-shot generalization to unseen object geometries is also observed on object-picking tasks. Our results suggest that simulated interaction is a key bridge between human videos and deployable dexterous manipulation policies, providing the missing physical supervision needed for scalable robot skill learning from Internet-scale human video data.

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BibTeX RIS

Ruoqu Chen, Feixiang Ruan, Liu Cao, Zihao Wang, Botian Xu, Shiqin Tong, Jiajun Liu, Mingzhi Pei, Chenyu Zhang, Wanli Xing, Kaifeng Zhang, Mengdi Xu. 2026-09-07. Dex-X: Learning Visual-Tactile Dexterous Manipulation From Human Videos with Simulated Interaction. https://arxiv.org/abs/2609.07747

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