Multimodal training by demonstration for robot-assisted surgery

Abstract

Improving surgical training has the potential to reduce medical errors and consequently to save many lives. We briefly present our efforts to improve this training for robot-assisted surgery. In particular, we explore how data collected from expert demonstrations can enhance the training efficiency for novices. Thus far, our results show that combining hand-over-hand training based on experts' motion data with trial and error training can improve the training outcomes in robotic and conventional laparoscopic surgery settings. We briefly describe our current efforts for exploring how gaze-based training methods, based on experts' eye gaze data, can improve the training outcomes as well.