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.

翻译：高精度操作任务，例如穿针引线，极具挑战性。生理学研究表明，低分辨率周边视觉与快速运动相结合可将手部移动至物体附近，而高分辨率中央凹视觉则用于实现手部对物体的精确定位。本研究结果表明，受人类基于视觉注意力的双分辨率视觉运动控制系统启发，基于深度模仿学习的方法能够解决穿针引线任务。首先，我们记录了远程操作机器人的操作者的眼动轨迹。然后，当线接近目标时，仅使用目光周围的高分辨率图像精确控制线位置，并利用低分辨率周边图像接近目标区域。实验结果表明，所提出的方法能够使用通用型机器人操作器实现高精度操作任务，同时提高计算效率。