Echo State Network for Soft Actuator Control

Abstract

Conventional model theories are not suitable to control soft-bodied robots as deformable materials present rapidly changing behaviors. Neuromorphic electronics are now entering the field of robotics, demonstrating that a highly integrated device can mimic the fundamental properties of a sensory synap-tic system, including learning and proprioception. This research work focuses on the physical implemen-tation of a reservoir computing-based network to actuate a soft-bodied robot. More specifically, modeling the hysteresis of a shape memory alloy (SMA) using echo state networks (ESN) in real-world situations represents a novel approach to enable soft machines with task-learning. In this work, we show that not only does our ESN model enable our SMA-based robot with locomotion, but it also discovers a successful strategy to do so. Compared to standard control modeling, established either by theoretical frameworks or from experimental data, here, we gained knowledge a pos-teriori, guided by the physical interactions between the trained model and the controlled actuator, interactions from which striking patterns emerged, and in-formed us about what type of locomotion would work best for our robot.