Scalable learning of dexterous manipulation remains bottlenecked by the difficulty of collecting natural, high-fidelity human demonstrations of multi-finger hands due to occlusion, complex hand kinematics, and contact-rich interactions. We present WHED, a wearable hand-exoskeleton system designed for in-the-wild demonstration capture, guided by two principles: wearability-first operation for extended use and a pose-tolerant, free-to-move thumb coupling that preserves natural thumb behaviors while maintaining a consistent mapping to the target robot thumb degrees of freedom. WHED integrates a linkage-driven finger interface with passive fit accommodation, a modified passive hand with robust proprioceptive sensing, and an onboard sensing/power module. We also provide an end-to-end data pipeline that synchronizes joint encoders, AR-based end-effector pose, and wrist-mounted visual observations, and supports post-processing for time alignment and replay. We demonstrate feasibility on representative grasping and manipulation sequences spanning precision pinch and full-hand enclosure grasps, and show qualitative consistency between collected demonstrations and replayed executions.

翻译：灵巧操作的可扩展学习仍然受限于难以收集自然、高保真度的多指手部人类演示数据，这主要是由于遮挡、复杂的手部运动学以及密集接触交互所致。我们提出了WHED，一种专为野外演示捕捉设计的可穿戴手部外骨骼系统，其设计遵循两大原则：以可穿戴性优先的操作支持长时间使用，以及采用姿态容忍、自由移动的拇指耦合机制，在保持与目标机器人拇指自由度一致映射的同时，保留自然的拇指行为。WHED集成了具有被动适配功能的连杆驱动手指接口、配备鲁棒本体感知的改进型被动手部，以及一个板载传感/电源模块。我们还提供了一个端到端的数据处理流程，能够同步关节编码器、基于增强现实的末端执行器位姿以及腕戴式视觉观测数据，并支持用于时间对齐与回放的后处理。我们在涵盖精确捏取和全手包握抓取的代表性抓取与操作序列上验证了系统的可行性，并展示了所采集演示与回放执行之间的定性一致性。