Perfecting Practice: Augmenting Human Sensorimotor Learning for Robot-Assisted Surgical Skill Acquisition

Telemanipulation, the human control of robotic manipulators that are remote in space or scale, allows human to accomplish complex dexterous manipulation tasks. Yet, it is difficult to learn how to teleoperate robotis in fine manipulation tasks like surgery. Recent work on sensorimotor learning in simple movements has demonstrated that implicit (unaware and automatic) and explicit (strategic) learning can occur simultaneously, and either compete with or complement each other. We will develop augmentations for accelerating and maximizing learning of skilled dexterous manipulation tasks, and to use a teleoperated surgical robot to test the effectiveness of these augmentations. We will work with surgeons to develop visual and haptic augmentations and characterize how the augmentations affect learning. Then we will develop methods to optimally map and synergistically combine visual and touch feedback to exploit both implicit and explicit learning for robot-assisted surgical skill acquisition.

This project will advance knowledge in engineering and neuroscience, informing robot and haptic device control, human sensorimotor learning, and cognitive and physical human-machine interaction. Our plan to operationalize basic findings about implicit and explicit motor learning via visual and haptic augmentations could transform the design of user training for robotic telemanipulation systems. Our activities comprise a tight interdisciplinary collaboration in robotics and human motor control, informed by international experts and studies in the application domain of surgery.