Abstract

This study aims to explore a control architecture that enables the control of a soft and flexible octopus-like arm for an object reaching task. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we discuss that the important factor of the control is not to regulate the arm muscles one by one but rather to control them globally with appropriate timing, and we propose an architecture equipped with a recurrent neural network (RNN). By setting the task environment for the reaching behavior, and training the network with an incremental learning strategy, we evaluate whether the network is then able to achieve the reaching behavior or not. As a result, we show that the RNN can successfully achieve the reaching behavior, exploiting the physical dynamics of the arm due to the timing based control.