Reinforcement learning selects multimodal locomotion strategies for bioinspired microswimmers

Abstract

Natural microswimmers exhibit multimodal locomotion strategies to achieve versatile navigation tasks such as finding nutrient sources, avoiding danger and migrating to new habitats. These multimodal locomotion strategies typically involve complex coordination of cell actuators (i.e., flagella) to generate translation, rotation and combined motions. Yet, it is generally difficult to establish a simple relationship between actuation and locomotion strategies due to the complex hydrodynamic coupling between the swimmer and the surrounding fluid. While many bioinspired microswimmers have been engendered, it remains challenging for these artificial swimmers to generate effective locomotion strategies for different functional tasks similar to their biological counterparts. Here, we explore a reinforcement learning (RL) method to enable a bioinspired microswimmer to select locomotion strategies based on different functional tasks. We illustrate this approach using a bioinspired model swimmer derived from \textit{Chlamydomonas reinhardtii}, which consists of a body sphere and two flagella spheres. We first demonstrate that this RL-powered bioinspired swimmer can select effective locomotion strategies that maximize displacement or minimize energy input by setting corresponding learning goals. We further illustrate how RL can enable the bioinspired swimmer to achieve multi-directional navigation via multimodal locomotion strategies that coordinately switch between forward and steering gaits. Our approach opens a new avenue to designing bioinspired microswimmers with multimodal locomotion capabilities.