Bayesian design optimization of biomimetic soft actuators

Abstract

The design of versatile soft actuators remains a challenging task, as it is a complex trade-off between robotic adaptability and structural complexity. Recently, researchers have used statistical and physical models to simulate the mechanical behavior of soft actuators . These simulations can help identify optimal actuator designs that fulfill specific robotic objectives. However, automated optimization of soft robots is a delicate balance between simplifying assumptions that reduce predictive fidelity and expensive simulations that limit design space exploration. Here we propose a generalized Bayesian optimization method to identify the designs of fiber-based biomimetic soft-robotic arms that minimize the actuation energy under arbitrary robotic control requirements. We use the reduced-order active filament theory as the overarching design paradigm and mechanical model, which enables a computationally robust and efficient optimization process. We evaluate the performance of our Bayesian optimization for a simple control objective in which the actuator has to reach a given target position. We show that our proposed optimization scheme outperforms a random-search baseline; it identifies more desirable designs faster and more frequently. Although we illustrate the performance of our approach for a single actuation problem, the derived method easily generalizes to the design optimization of fiber-based actuators under a large family of robotic applications .

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