  Rectilinear crawling locomotion is a primitive and common mode of locomotion
in slender, soft-bodied animals. It requires coordinated contractions that
propagate along a body that interacts frictionally with its environment. We
propose a simple approach to understand how these coordinations arise in a
neuromechanical model of a segmented, soft-bodied crawler via an iterative
process that might have both biological antecedents and technological
relevance. Using a simple reinforcement learning algorithm, we show that an
initial all-to-all neural coupling converges to a simple nearest-neighbor
neural wiring that allows the crawler to move forward using a localized wave of
contraction that is qualitatively similar to what is observed in D.
melanogaster larvae and used in many biomimetic solutions. The resulting
solution is a function of how we weight gait regularization in the reward, with
a tradeoff between speed and robustness to proprioceptive noise. Overall, our
results, which embed the brain-body-environment triad in a learning scheme, has
relevance for soft robotics while shedding light on the evolution and
development of locomotion.
