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The computational design of soft underwater swimmers is challenging becauseof the high degrees of freedom in soft-body modeling. In this paper, we presenta differentiable pipeline for co-designing a soft swimmer's geometry andcontroller. Our pipeline unlocks gradient-based algorithms for discoveringnovel swimmer designs more efficiently than traditional gradient-freesolutions. We propose Wasserstein barycenters as a basis for the geometricdesign of soft underwater swimmers since it is differentiable and can naturallyinterpolate between bio-inspired base shapes via optimal transport. Bycombining this design space with differentiable simulation and control, we canefficiently optimize a soft underwater swimmer's performance with fewersimulations than baseline methods. We demonstrate the efficacy of our method onvarious design problems such as fast, stable, and energy-efficient swimming anddemonstrate applicability to multi-objective design.

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