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Dexterous Teleoperation of 20-DoF ByteDexter Hand via Human Motion Retargeting

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arxiv 2507.03227 v1 pith:YIYU4KN6 submitted 2025-07-04 cs.RO

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
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Liqun Huang Hao Niu Wei Xu Haoxiang Zhang Zhengming Zhu Hang Li Zeyu Ren
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classification cs.RO
keywords humandexterityhandteleoperationcontroldatademonstrationdexterous
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 objects. Experimental results demonstrate its intuitive teleoperation interface with real--time control and the ability to generate high--quality demonstration data. Please refer to the accompanying video for further details.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning

    cs.RO 2026-06 unverdicted novelty 6.0

    A wearable interface with a shared dexterous hand module enables retargeting-free teleoperation and matched data collection, yielding policies with 88.75% average success across eight real-robot tasks that generalize ...

  2. Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention

    cs.RO 2026-05 unverdicted novelty 6.0

    HandITL blends human intent with policy execution to eliminate gesture jumps in dexterous VLA interventions, cutting jitter by 99.8%, grasp failures by 87.5%, and yielding 19% better refined policies.

  3. Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention

    cs.RO 2026-05 unverdicted novelty 6.0

    HandITL enables seamless human intervention in VLA policies for bimanual dexterous manipulation, cutting jitter by 99.8% and improving refined policies by 19% over standard teleoperation.

  4. AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance

    cs.RO 2026-07 conditional novelty 5.5

    Self-supervised fingertip mapping with few-shot human anchors and a pinch contact classifier yields calibration-free, more intuitive retargeting across diverse human-like robot hands.