发表机构
Delta Intelligence（三角洲智能）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SEED-UMI框架，通过人与机器人共享外骨骼实现一对一灵巧演示，将重定向转为配对跨具身监督，在五个接触丰富任务上数据收集效率提升3.0倍，平均成功率70.0%。
AI 中文摘要
灵巧手的模仿学习受限于难以收集接触丰富的演示数据，这些数据能忠实地迁移到机器人上。先前的可穿戴外骨骼系统仅记录人体侧数据，并通过在自由空间中校准的开环映射进行重定向，这在接触情况下会退化。我们提出SEED-UMI框架，其中人和机器人佩戴相同的外骨骼：关节编码器成为物理共享的测量装置，安装在外骨骼上的腕部摄像头在人类数据收集和机器人策略执行期间观察相同的外部机制。这将重定向转化为配对的跨具身监督，并让策略直接基于原始的外骨骼中心腕部图像进行训练，无需分割或修复。在五个接触丰富的任务中，SEED-UMI相比基于外骨骼的遥操作实现了3.0倍的数据收集效率提升，并达到了70.0%的平均执行成功率。
英文摘要
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.
