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

• URL: http://arxiv.org/abs/2609.11753v1
• Date: Thu, 10 Sep 2026 16:08:58 GMT
• Title: SEED-UMI: Sharing the Exoskeleton between human and robot for onE-to-one Dexterous demonstration
• Abstract summary: We present SEED-UMI, a framework in which both the human and the robot wear the same exoskeleton encoders.<n>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.
• Score: 8.989813212279424
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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