Gait learning for soft microrobots controlled by light fields

TL;DR

This paper introduces a data-efficient probabilistic learning method using Bayesian Optimization and Gaussian Processes to optimize gait in light-controlled soft microrobots, achieving significant performance improvements with limited experiments.

Contribution

It presents a novel probabilistic learning scheme for gait optimization in soft microrobots, addressing variability and data limitations without relying on extensive experiments.

Findings

115% improvement in locomotion performance

Validated approach with only 20 experimental tests

Robust gait optimization across different microrobot samples

Abstract

Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize their locomotion performance in a given environment and used to adapt them to changing conditions. Albeit, because of the lack of accurate locomotion models, and given the intrinsic variability among microrobots, analytical control design is not possible. Common data-driven approaches, on the other hand, require running prohibitive numbers of experiments and lead to very sample-specific results. Here we propose a probabilistic learning approach for light-controlled soft microrobots based on Bayesian Optimization (BO) and Gaussian Processes (GPs). The proposed approach results in a learning scheme that is data-efficient, enabling gait optimization with a limited experimental budget, and robust against differences among microrobot samples. These features are obtained by designing the learning scheme through the comparison of different GP priors and BO settings on a semi-synthetic data set. The developed learning scheme is validated in microrobot experiments, resulting in a 115% improvement in a microrobot's locomotion performance with an experimental budget of only 20 tests. These encouraging results lead the way toward self-adaptive microrobotic systems based on light-controlled soft microrobots and probabilistic learning control.