Fine Tuning Swimming Locomotion Learned from Mosquito Larvae

In prior research, we analyzed the backwards swimming motion of mosquito larvae, and created a parametrized approximation in a Computational Fluid Dynamics simulation. Since the parameterized swimming motion is replicated from observed larvae, it is not necessarily the most efficient locomotion. In this project, we further optimize this swimming locomotion for the simulated platform, using Reinforcement Learning to guide local parameter updates. Since the majority of the computation cost arises from the Computational Fluid Dynamics model, we additionally train a deep neural network 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.