Using sim-to-real reinforcement learning to train robots to do simple tasks in broad environments

A team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim-to-real reinforcement learning to train them. In their study, published in the journal Science Robotics, the group trained a robot to walk in unfamiliar environments while it carried different loads, all without toppling over.

This post was originally published on this site