Learning swimming escape patterns for larval fish under energy constraints
This work addresses energy-efficient swimming for larval fish and aquatic robots, but it is incremental as it builds on prior optimization methods with a new algorithmic approach.
The study tackled the problem of discovering energy-efficient swimming escape patterns for larval fish using reinforcement learning, resulting in the identification of the C-start mechanism and more efficient patterns that maximize distance with short bursts and gliding phases.
Swimming organisms can escape their predators by creating and harnessing unsteady flow fields through their body motions. Stochastic optimization and flow simulations have identified escape patterns that are consistent with those observed in natural larval swimmers. However, these patterns have been limited by the specification of a particular cost function and depend on a prescribed functional form of the body motion. Here, we deploy reinforcement learning to discover swimmer escape patterns for larval fish under energy constraints. The identified patterns include the C-start mechanism, in addition to more energetically efficient escapes. We find that maximizing distance with limited energy requires swimming via short bursts of accelerating motion interlinked with phases of gliding. The present, data efficient, reinforcement learning algorithm results in an array of patterns that reveal practical flow optimization principles for efficient swimming and the methodology can be transferred to the control of aquatic robotic devices operating under energy constraints.