Title: Gait learning for soft microrobots controlled by light fields,
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.