DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation

TL;DR

DiffAqua introduces a differentiable design pipeline for soft underwater swimmers, enabling efficient optimization of shape and control through gradient-based methods and shape interpolation.

Contribution

It presents a novel differentiable pipeline using Wasserstein barycenters for shape interpolation, improving design efficiency over traditional methods.

Findings

Efficient optimization of swimmer performance with fewer simulations.

Successful design of fast, stable, and energy-efficient swimmers.

Applicability to multi-objective design problems.

Abstract

The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.