New AI strategy enables robots to rapidly adapt to real world environments

The RMA system combines a base policy -- the algorithm by which the robot determines how to move -- with an adaptation module. The base policy uses reinforcement learning to develop controls for sets of extrinsic variables in the environment. This is learned in simulation, but that alone is not enough to prepare the legged robot for the real world because the robot's onboard sensors cannot directly measure all possible variables in the environment. To solve this, the adaptation module directs the robot to teach itself about its surroundings using information based on its own body movements. For example, if a robot senses that its feet are extending farther, it may surmise that the surface it is on is soft and will adapt its next movements accordingly.