A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning

Abstract:We present a visuo-tactile data-collection system that generates temporally structured, contact-rich demonstrations for imitation learning. Conventional systems often decouple the operator from contact forces, which hinders the demonstration of subtle force modulation. Our system introduces a direct-drive gripper that the operator actuates with the fingers, preserving natural haptic feedback. Integrated visual sensors and custom tactile arrays capture image streams and contact geometry. A handle-mounted push button enables the operator to annotate the task's temporal structure in real time by marking task-critical regions. By fusing in-hand force perception with in-situ temporal annotation, the system produces multimodal datasets designed for coarse-to-fine learning algorithms that exploit structural task knowledge, enabling the development of high-quality manipulation policies.

Robotics (cs.RO)

arXiv:2605.08757 [cs.RO]

(or arXiv:2605.08757v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.08757 Focus to learn more arXiv-issued DOI via DataCite

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