Rapid and Cheap Learning by Exploiting Biarticular Muscles — A Case Study With a Two-Dimensional 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 which induce long-distant physical interaction between the body segments compared to the one only with monoarticular muscles. This strongly suggests the fact that a certain amount of computation should be offloaded from the brain into its body, which allows robots to emerge various with interesting functionalities.