Planar Modeling and Sim-to-Real of a Tethered Multimaterial Soft Swimmer Driven by Peano-HASELs

TL;DR

This paper presents a streamlined design, fabrication, and differentiable simulation pipeline for a tethered multimaterial soft swimmer driven by Peano-HASELs, enabling improved control, efficiency, and optimization of soft robotic swimmers.

Contribution

It introduces an efficient pipeline combining additive manufacturing and differentiable simulation for soft robotic swimmers, facilitating rapid design, fabrication, and sim-to-real transfer.

Findings

Voltage and frequency affect swimming speed.

Optimal actuation parameters exist for maximum speed.

Differentiable simulation enables effective control and shape optimization.

Abstract

Soft robotics has the potential to revolutionize robotic locomotion, in particular, soft robotic swimmers offer a minimally invasive and adaptive solution to explore and preserve our oceans. Unfortunately, current soft robotic swimmers are vastly inferior to evolved biological swimmers, especially in terms of controllability, efficiency, maneuverability, and longevity. Additionally, the tedious iterative fabrication and empirical testing required to design soft robots has hindered their optimization. In this work, we tackle this challenge by providing an efficient and straightforward pipeline for designing and fabricating soft robotic swimmers equipped with electrostatic actuation. We streamline the process to allow for rapid additive manufacturing, and show how a differentiable simulation can be used to match a simplified model to the real deformation of a robotic swimmer. We perform several experiments with the fabricated swimmer by varying the voltage and actuation frequency of the swimmer's antagonistic muscles. We show how the voltage and frequency vary the locomotion speed of the swimmer while moving in liquid oil and observe a clear optimum in forward swimming speed. The differentiable simulation model we propose has various downstream applications, such as control and shape optimization of the swimmer; optimization results can be directly mapped back to the real robot through our sim-to-real matching.