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arXiv · 2601.01651

DemoBot: Efficient Learning of Bimanual Manipulation with Dexterous Hands From Third-Person Human Videos

Yucheng Xu·Xiaofeng Mao·Elle Miller·Xinyu Yi·Yang Li·Zhibin Li·Robert B. Fisher
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Abstract

This work presents DemoBot, a learning framework that enables a dual-arm, multi-finger robotic system to acquire complex manipulation skills from a single unannotated RGB-D video demonstration. The method extracts structured motion trajectories of both hands and objects from raw video data. These trajectories serve as motion priors for a novel reinforcement learning (RL) pipeline that learns to refine them through contact-rich interactions, thereby eliminating the need to learn from scratch. To address the challenge of learning long-horizon manipulation skills, we introduce: (1) Temporal-segment based RL to enforce temporal alignment of the current state with demonstrations; (2) Success-Gated Reset strategy to balance the refinement of readily acquired skills and the exploration of subsequent task stages; and (3) Event-Driven Reward curriculum with adaptive thresholding to guide the RL learning of high-precision manipulation. The novel video processing and RL framework successfully achieved long-horizon synchronous and asynchronous bimanual assembly tasks, offering a scalable approach for direct skill acquisition from human videos.

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Yucheng Xu, Xiaofeng Mao, Elle Miller, Xinyu Yi, Yang Li, Zhibin Li, Robert B. Fisher. 2026-01-04. DemoBot: Efficient Learning of Bimanual Manipulation with Dexterous Hands From Third-Person Human Videos. https://arxiv.org/abs/2601.01651

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