Behavior switching using reservoir computing for a soft robotic arm

Abstract

Soft robots have significant advantages over traditional robots made of rigid materials. However, controlling this type of robot by conventional approaches is difficult. Reservoir computing has been demonstrated to be an effective approach for achieving rapid learning in benchmark tasks and conventional robots. In this study, we investigated the feasibility and capacity of the reservoir computing approach to embedding and switching between multiple behaviors in a on-line manner in a soft robotic arm. The result shows that this approach can successfully achieve this task.