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

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.

Robotics (cs.RO)

arXiv:2609.11753 [cs.RO]

(or arXiv:2609.11753v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.11753 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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