  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.
