A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866909029578571776 |
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| author | Kim, Yeseung Oh, Nayoung Park, Jun Thamronglak, Teetat Park, Daehyung |
| author_facet | Kim, Yeseung Oh, Nayoung Park, Jun Thamronglak, Teetat Park, Daehyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08757 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning Kim, Yeseung Oh, Nayoung Park, Jun Thamronglak, Teetat Park, Daehyung Robotics 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. |
| title | A Visuo-Tactile Data Collection System with Haptic Feedback for Coarse-to-Fine Imitation Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.08757 |