Fine Tuning Swimming Locomotion Learned from Mosquito Larvae

TL;DR

This paper enhances mosquito larvae swimming models by applying reinforcement learning and deep learning to optimize locomotion efficiency, reducing computational costs and improving movement performance.

Contribution

It introduces a novel approach combining reinforcement learning and deep force modeling to optimize swimming locomotion in CFD-based mosquito larvae models.

Findings

Reinforcement learning effectively improves swimming parameters.

Deep learning models replicate fluid forces efficiently.

Optimized locomotion outperforms initial parameterizations.

Abstract

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.