Dexterous Teleoperation of 20-DoF ByteDexter Hand via Human Motion Retargeting
Ruoshi Wen, Jiajun Zhang, Guangzeng Chen, Zhongren Cui, Min Du, Yang Gou, Zhigang Han, Junkai Hu, Liqun Huang, Hao Niu, Wei Xu, Haoxiang Zhang, Zhengming Zhu, Hang Li, Zeyu Ren

TL;DR
This paper presents a novel teleoperation system that enables high-fidelity, real-time control of a 20-DoF robotic hand through human motion retargeting, facilitating dexterous manipulation and data collection.
Contribution
It introduces a biomimetic 20-DoF robotic hand with an optimization-based retargeting system for seamless human motion transfer in teleoperation.
Findings
Successful in-hand manipulation and organization tasks
High-quality demonstration data generated in real-time
Intuitive interface enabling complex dexterous control
Abstract
Replicating human--level dexterity remains a fundamental robotics challenge, requiring integrated solutions from mechatronic design to the control of high degree--of--freedom (DoF) robotic hands. While imitation learning shows promise in transferring human dexterity to robots, the efficacy of trained policies relies on the quality of human demonstration data. We bridge this gap with a hand--arm teleoperation system featuring: (1) a 20--DoF linkage--driven anthropomorphic robotic hand for biomimetic dexterity, and (2) an optimization--based motion retargeting for real--time, high--fidelity reproduction of intricate human hand motions and seamless hand--arm coordination. We validate the system via extensive empirical evaluations, including dexterous in-hand manipulation tasks and a long--horizon task requiring the organization of a cluttered makeup table randomly populated with nine…
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