An efficient decentralized learning by exploiting biarticular muscles - A case study with a 2D serpentine robot -

Abstract

This study is intended to deal with the interplay between control and mechanical systems, and to discuss the "brain-body interaction as it should be" particularly from the viewpoint of learning. To this end, we have employed a decentralized control of a two-dimensional serpentine robot consisting of several identical body segments as a practical example. The preliminary simulation results derived indicate that the convergence of decentralized learning of locomotion control can be significantly improved even with an extremely simple learning algorithm, i.e., a gradient method, by introducing biarticular muscles compared to the one only with monoarticular muscles. This strongly suggests the fact that a certain amount of computation should be off loaded from the brain into its body, which allows robots to emerge various interesting functionalities.