'Fake' data helps robots learn the ropes faster: A way to expand training data sets for manipulation tasks improves the performance of robots by 40% or more

Developed by robotics researchers at the University of Michigan, it could cut learning time for new materials and environments down to a few hours rather than a week or two. In simulations, the expanded training data set improved the success rate of a robot looping a rope around an engine block by more than 40% and nearly doubled the successes of a physical robot for a similar task. That task is among those a robot mechanic would need to be able to do with ease. But using today's methods, learning how to manipulate each unfamiliar hose or belt would require huge amounts of data, likely gathered for days or weeks, says Dmitry Berenson, U-M associate professor of robotics and senior author of a paper presented today at Robotics: Science and Systems in New York City. In that time, the robot would play around with the hose -- stretching it, bringing the ends together, looping it around obstacles and so on -- until it understood all the ways the hose could move.