外观
论文arXiv 2610.0769222 天前
ExoBridge：通过人类肢体耦合学习裸手到手戴式外骨骼的映射
原文链接：ExoBridge: Learning a Bare Hand to Hand-Worn Exoskeleton Mapping through Human Limb Coupling（arXiv 2610.07692 · 发布日期：2026-10-06）
ExoBridge: Learning a Bare Hand to Hand-Worn Exoskeleton Mapping through Human Limb Coupling
ExoBridge利用双手肢体耦合，从裸手视频预测外骨骼运动与触觉状态，并在四个操作任务演示上验证。
核心要点
- 人类视频可扩展灵巧学习经验，但裸手交互下难以获得运动和触觉监督。
- 采集时一只手裸手提供视觉，另一只手佩戴传感外骨骼提供同步测量。
- 时序视觉模型从裸手视频预测指尖接触、触觉强度和相对编码器运动。
- 在1,215次、四项任务演示上，任意接触AUROC为0.916，触觉强度相关为0.790。
验证范围与局限：验证范围为四个操作任务的1,215次演示及留出采集会话。
英文摘要 (Abstract)
Human video offers a scalable source of experience for dexterous robot learning, but obtaining motion and tactile supervision while preserving bare hand interaction remains challenging. We present ExoBridge, a framework that leverages human limb coupling to learn a bridging function from bare hand video to the motion and tactile state of a sensorized exoskeleton. Our central idea is to use coordinated bimanual behavior to connect an uninstrumented visual source with a measured manipulation interface. During collection, one hand remains bare and provides visual observations, while the opposite hand wears the exoskeleton and supplies synchronized motion and tactile measurements. These paired demonstrations train a temporal visual model to predict fingertip contact, continuous tactile intensity, and relative encoder motion from bare hand video alone. The exoskeleton defines an intermediate state space whose motion coordinates are linked to a dexterous robot hand through existing calibration. Evaluation on 1,215 demonstrations across four manipulation tasks uses held out collection sessions and yields a pooled any contact AUROC of 0.916 and a Pearson correlation of 0.790 for tactile intensity. The learned bridge also predicts relative changes in exoskeleton configuration from bare hand video. These results demonstrate that human limb coupling can turn exoskeleton measurements into supervision for bare hand video, establishing a learned bridge between human visual demonstrations and a robot oriented manipulation interface.
作者：Ruitong Tian, Xianyao Li, Noah B. Wilson, Fang Xu, Eric Jing Du
来源
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