A hybrid learning architecture based on neural networks for adaptive control of a walking machine

Abstract

Online learning of complex control behaviour of autonomous mobile robots is one of the current research topics. In this article a hybrid learning architecture based on self-organizing neural networks for online adaptivity is presented. The hybrid concept integrates different learning methods and task-oriented representations as well as available domain knowledge. The proposed concept is used for reinforcement learning of control strategies on different control levels on a walking machine.