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SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration

2026-09-10 16:08:58
Tengbo Yu, Jiahao Wu, Daohan Li, Bingxu Chen, Hao Liu, Xiaojian Ma, Hangxin Liu
arXiv_RO
       
Abstract

Imitation learning for dexterous hands is bottlenecked by the difficulty of collecting contact-rich demonstrations that transfer faithfully to the robot. Prior wearable-exoskeleton systems record only on the human side and retarget via open-loop mappings calibrated in free space, which degrade under contact. We present SEED-UMI, a framework in which both the human and the robot wear the same exoskeleton: joint encoders become a physically shared measurement, and wrist cameras mounted to the exoskeleton observe the same outer mechanism during both human data collection and robot policy rollouts. This turns retargeting into paired cross-embodiment supervision and lets policies train directly on raw exoskeleton-centric wrist images, without segmentation or inpainting. On five contact-rich tasks, SEED-UMI achieves 3.0x greater data collection efficiency than exoskeleton-based teleoperation and a 70.0% average rollout success rate.

Abstract (translated)

URL

https://arxiv.org/abs/2609.11753

PDF

https://arxiv.org/pdf/2609.11753.pdf

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