Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number

TL;DR

This study uses reinforcement learning to discover optimal limb coordination patterns for a swimmer at zero Reynolds number, revealing that a metachronal wave emerges as the most efficient stroke across paddle spacings.

Contribution

It demonstrates that reinforcement learning can autonomously discover biologically relevant metachronal paddling patterns at low Reynolds number.

Findings

Metachronal wave emerges at tight paddle spacings.

Different coordination patterns appear at wider spacings.

Back-to-front wave-like stroke is most efficient regardless of paddle number.

Abstract

Metachronal paddling is a swimming strategy in which an organism oscillates sets of adjacent limbs with a constant phase lag, propagating a metachronal wave through its limbs and propelling it forward. This limb coordination strategy is utilized by swimmers across a wide range of Reynolds numbers, which suggests that this metachronal rhythm was selected for its optimality of swimming performance. In this study, we apply reinforcement learning to a swimmer at zero Reynolds number and investigate whether the learning algorithm selects this metachronal rhythm, or if other coordination patterns emerge. We design the swimmer agent with an elongated body and pairs of straight, inflexible paddles placed along the body for various fixed paddle spacings. Based on paddle spacing, the swimmer agent learns qualitatively different coordination patterns. At tight spacings, a back-to-front metachronal wave-like stroke emerges which resembles the commonly observed biological rhythm, but at wide spacings, different limb coordinations are selected. Across all resulting strokes, the fastest stroke is dependent on the number of paddles, however, the most efficient stroke is a back-to-front wave-like stroke regardless of the number of paddles.