Learning to swim in potential flow

Abstract

Fish swim by coordinating their shape changes with the fluid environment to produce forward swimming or turning gaits. We use model-free reinforcement learning to learn shape coordinations that lead to robust turning and forward swimming motions in the context of a simple three-link fish in a potential flow environment. We show that the optimal control policies arrived at by reinforcement learning are interpretable via shape space analysis in driftless environment and are robust to the presence of drift-related perturbations.

Topics & Concepts