ROBOT TRAJECTORY LEARNING THROUGH PRACTICE.

An algorithm that uses trajectory-following errors to improve a feedforward command to a robot is presented. This approach to robot learning is based on explicit modeling of the robot and uses an inverse of the robot model as part of a learning operator which processes the trajectory errors. Results are presented from a successful implementation of this procedure on the MIT serial-link direct-drive arm. It is shown that more accurate robot models improve trajectory learning performance, and learning algorithms do not reduce the need for good models in robot control.