Enhancing the performance of adaptive iterative learning control with reinforcement learning

In this study we propose a new method to enhance the performance of iterative learning control (ILC). We focus on robotic tasks dealing with adaptation to the unknown or partially known environment, where the robot has to learn the environment geometry in order to perform the desired task with the given reference forces and torques. The initial motion trajectories are obtained by kinesthetic teaching, whereas the required forces and torques are prescribed by the task. We are interested in incremental learning, which assures smooth and safe operation, aiming at handling of delicate, fragile objects, such as objects made of glass. In order to achieve these goals we propose a new adaptive ILC scheme, where the adaptation is supervised by reinforcement learning. We also show how to apply ILC to orientational motion, taking into account the curved geometry of SO(3). The performance of the proposed algorithm is verified on a bi-manual glass wiping task.