  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.
