Fish locomotion emerges from a diversity of interactions among deformable structures, surrounding fluids and neuromuscular activations, i.e., fluid-structure interactions (FSI) controlled by fish's motor systems. Previous studies suggested that such motor-controlled FSI may possess embodied traits. However, their implications in motor learning, neuromuscular control, gait generation, and swimming performance remain to be uncovered. Using robot models, we studied how swimming behaviours emerged from the FSI and the embodied traits. We developed modular robots with various designs and used Central Pattern Generators (CPGs) to control the torque acting on robot body. We used reinforcement learning to learn CPG parameters to maximize the swimming speed. The results showed that motor frequency converged faster than other parameters, and the emergent swimming gaits were robust against disruptions applied to motor control. For all robots and frequencies tested, swimming speed was proportional to the mean undulation velocity of body and caudal-fin combined, yielding an invariant, undulation-based Strouhal number. The Strouhal number also revealed two fundamental classes of undulatory swimming in both biological and robotic fishes. The robot actuators also demonstrated diverse functions as motors, virtual springs, and virtual masses. These results provide novel insights into the embodied traits of motor-controlled FSI for fish-inspired locomotion.

翻译：鱼类游动源于可变形结构、周围流体与神经肌肉激活之间多样化的相互作用，即受鱼类运动系统控制的流固耦合。先前研究表明，这类运动控制的流固耦合可能具备具身特性。然而，这些特性在运动学习、神经肌肉控制、步态生成及游泳性能方面的影响仍有待揭示。我们采用机器人模型，研究了游泳行为如何从流固耦合与具身特性中涌现。我们开发了多种构型的模块化机器人，并使用中枢模式发生器控制作用于机器人身体的扭矩。通过强化学习优化CPG参数以最大化游动速度。结果表明，运动频率的收敛速度优于其他参数，且涌现出的游泳步态对运动控制扰动具有鲁棒性。在所有测试的机器人和频率下，游动速度与躯干及尾鳍联合平均波动速度呈正比，从而产生基于波动的不变斯特劳哈尔数。该斯特劳哈尔数还揭示了生物鱼与机器鱼两类波动游动的基本模式。机器人执行器还展现出电机、虚拟弹簧和虚拟质量等多样化功能。这些结果为受鱼启发的运动控制流固耦合具身特性提供了新见解。