Algorithms Enable Robots to Avoid Obstacles and Run in the Wild

The team addressed this challenge by simulating the mismatch by randomizing the two sets of inputs. The researchers refer to this technique as multi-modal delay randomization, and they then used the used and randomized inputs to train a reinforcement learning policy. The approach enabled the robot to make decisions quickly while it was navigating, as well as anticipate changes in its environment. These abilities allowed the robot to move and maneuver obstacles faster on different types of terrains, all without assistance from a human operator.