WHED: A Wearable Hand Exoskeleton for Natural, High-Quality Demonstration Collection
This addresses the bottleneck in scalable learning of dexterous manipulation for robotics by enabling in-the-wild demonstration capture, though it appears incremental as it builds on existing exoskeleton and sensing technologies.
The paper tackled the challenge of collecting natural, high-fidelity human demonstrations for dexterous manipulation by developing WHED, a wearable hand-exoskeleton system, and demonstrated its feasibility on grasping and manipulation tasks with qualitative consistency.
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.