A robot, trained for the first time by watching videos of seasoned surgeons, executed the same surgical procedures as skillfully as the human doctors.
The successful use of imitation learning to train surgical robots eliminates the need to program robots with each individual move required during a medical procedure and brings the field of robotic surgery closer to true autonomy, where robots could perform complex surgeries without human help.
The findings, led by Johns Hopkins University researchers, were recently spotlighted at the Conference on Robot Learning in Munich, a top event for robotics and machine learning.
Authors from Johns Hopkins include ECE PhD student Samuel Schmidgall; Associate Research Engineer and Malone affiliate Anton Deguet; and Associate Professor of Mechanical Engineering Marin Kobilarov. Stanford University authors are PhD student Tony Z. Zhao and Assistant Professor Chelsea Finn.