Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation
This work provides a novel approach for improving the precision and efficiency of dexterous robot manipulation, particularly for complex tasks like needle threading, benefiting robotics researchers and practitioners.
This paper addresses the challenge of high-precision manipulation tasks like needle threading by developing a deep imitation learning method inspired by human gaze-based dual-resolution visuomotor control. The method successfully solves the needle threading task using a general-purpose robot manipulator and improves computational efficiency.
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.