Propeller-powered underwater vehicles have long helped scientists explore and monitor aquatic environments. But they're limited by their own mechanics: spinning blades can snag on vegetation, stir up sediment, and startle the wildlife they're often sent to study, making them poorly suited to shallow creeks, dense weeds, or close encounters with fish. That's one reason roboticists have spent years building machines that swim like fish instead, bending their bodies rather than spinning a propeller. The catch is that most fish-inspired robots are built for one size and one job, so scaling them up or down usually means starting from scratch. A team of engineers at EPFL and New York University (NYU) says it has found a way to solve that problem.

In prior research, we analyzed the backwards swimming motion of mosquito larvae, parameterized it, and replicated it in a Computational Fluid Dynamics (CFD) model. Since the parameterized swimming motion is copied from observed larvae, it is not necessarily the most efficient locomotion for the model of the swimmer. In this project, we further optimize this copied solution for the swimmer model. We utilize Reinforcement Learning to guide local parameter updates. Since the majority of the computation cost arises from the CFD model, we additionally train a deep learning model to replicate the forces acting on the swimmer model. We find that this method is effective at performing local search to improve the parameterized swimming locomotion.