Teaching a Robot Dog New Tricks: Diverse Quadruped Skills via Combined Reinforcement and Imitation Learning with Adversarial Task Selection
The method trains a single robot policy to perform multiple distinct skills like walking, digging, and hopping by first training several teacher policies for each skill. A student policy then learns these tasks via combined reinforcement learning and imitation learning objectives under an adversarial selection process focused on difficult tasks. This allows the robot to compose novel behaviors, such as crawling.