Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866915296712851456 |
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| author | Kim, Heecheol Ohmura, Yoshiyuki Kuniyoshi, Yasuo |
| author_facet | Kim, Heecheol Ohmura, Yoshiyuki Kuniyoshi, Yasuo |
| contents | A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to transport the hand into the vicinity of an object, and using high-resolution foveated vision to achieve the accurate homing of the hand to the object. The results of this study demonstrate that a deep imitation learning based method, inspired by the gaze-based dual resolution visuomotor control system in humans, can solve the needle threading task. First, we recorded the gaze movements of a human operator who was teleoperating a robot. Then, we used only a high-resolution image around the gaze to precisely control the thread position when it was close to the target. We used a low-resolution peripheral image to reach the vicinity of the target. The experimental results obtained in this study demonstrate that the proposed method enables precise manipulation tasks using a general-purpose robot manipulator and improves computational efficiency. Data from this and related works are available at: https://sites.google.com/view/multi-task-fine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2102_01295 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation Kim, Heecheol Ohmura, Yoshiyuki Kuniyoshi, Yasuo Robotics Artificial Intelligence A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to transport the hand into the vicinity of an object, and using high-resolution foveated vision to achieve the accurate homing of the hand to the object. The results of this study demonstrate that a deep imitation learning based method, inspired by the gaze-based dual resolution visuomotor control system in humans, can solve the needle threading task. First, we recorded the gaze movements of a human operator who was teleoperating a robot. Then, we used only a high-resolution image around the gaze to precisely control the thread position when it was close to the target. We used a low-resolution peripheral image to reach the vicinity of the target. The experimental results obtained in this study demonstrate that the proposed method enables precise manipulation tasks using a general-purpose robot manipulator and improves computational efficiency. Data from this and related works are available at: https://sites.google.com/view/multi-task-fine. |
| title | Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2102.01295 |