Control of hyper-redundant robot using QDSEGA

We consider a flexible autonomous system. To realize the system, we employ a hyper-redundant system (a flexible hardware system) and reinforcement learning controller "QDSEGA" (Q-learning with structuring exploration space based on genetic algorithm) which is a flexible software system. In this paper we apply QDSEGA for controlling of the hyper-redundant robot. To demonstrate the effectiveness, a task of acquisition of locomotion patterns is applied to a multi-legged formation and a snake-like formation, from which an effective locomotion is obtained.