  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.
