用强化学习模拟低雷诺数下桨动虫的协调游动，发现后向前波是最高效模式。
Optimizing Metachronal Paddling with Reinforcement Learning at Low Reynolds Number
- 通过强化学习让虚拟生物自主探索桨叶协调方式。
- 后向前波形协调在所有配置中均最高效，但最快取决于桨叶数量。
- 适用于研究生物游动机制或优化微尺度机器人设计。
metachronal paddling 是一种通过相邻肢体以固定相位差振荡，形成波动推进的游泳策略，在多种雷诺数下被广泛使用，暗示其在游动性能上的优越性。本研究将强化学习应用于零雷诺数下的泳者模型，探究学习算法是否会选择这种生物常见的 metachronal 节律，或催生其他协调模式。泳者由细长身体和沿体侧固定间距排列的直形刚性桨叶构成。根据桨叶间距不同，学习到的协调模式有显著差异：紧密间距时出现类似生物界的后向前 metachronal 波，而宽间距时则选择不同模式。所有生成的游动中，最快速度依赖于桨叶数量；然而，无论桨叶数量如何，最高效的游动始终是后向前波形模式。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。