JRM Vol.34 No.2 pp. 413-421
doi: 10.20965/jrm.2022.p0413


Echo State Network for Soft Actuator Control

Cedric Caremel, Matthew Ishige, Tung D. Ta, and Yoshihiro Kawahara

Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

September 20, 2021
December 24, 2021
April 20, 2022
echo state networks (ESN), reservoir computing (RC), shape memory alloys (SMAs), soft robotics
Echo State Network for Soft Actuator Control

SMA-based soft robot controlled by ESN

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 synaptic system, including learning and proprioception. This research work focuses on the physical implementation 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 posteriori, guided by the physical interactions between the trained model and the controlled actuator, interactions from which striking patterns emerged, and informed us about what type of locomotion would work best for our robot.

Cite this article as:
Cedric Caremel, Matthew Ishige, Tung D. Ta, and Yoshihiro Kawahara, “Echo State Network for Soft Actuator Control,” J. Robot. Mechatron., Vol.34, No.2, pp. 413-421, 2022.
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Last updated on May. 20, 2022