Task-Level Robot Learning: Ball Throwing

Abstract

We are investigating how to program robots so that they learn tasks from practice. One method, task-level learning, provides advantages over simply perfecting models of the robot's lower level systems. Task-level learning can compensate for the structural modeling errors of the robot's lower level control systems and can speed up the learning process by reducing the degrees of freedom of the models to be learned. We demonstrate two general learning procedures--fixed-model learning and refined-model learning---on a ball-throwing robot system. Both learning approaches refine the task command based on the performance error of the system, while they ignore the intermediate variables separating the lower level systems. We provide both experimental and theoretical evidence that task-level learning can improve a robot's performance of a task.