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 this coordination arises 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-neighbour neural wiring that allows the crawler to move forward using a localized wave of contraction that is qualitatively similar to what is observed in Drosophila 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 trade-off between speed and robustness to proprioceptive noise. Overall, our results, which embed the brain–body–environment triad in a learning scheme, have relevance for soft robotics while shedding light on the evolution and development of locomotion.

中文翻译：

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通过强化学习协调爬行


直线爬行运动是细长软体动物的一种原始且常见的运动模式。它需要协调的收缩，沿着与环境摩擦相互作用的身体传播。我们提出了一种简单的方法来理解这种协调是如何通过可能具有生物学背景和技术相关性的迭代过程在分段软体爬行者的神经力学模型中产生的。使用简单的强化学习算法，我们表明初始的全对全神经耦合收敛到简单的最近邻神经连线，该神经连线允许爬行器使用局部收缩波向前移动，该收缩波在质量上类似于在果蝇幼虫并用于许多仿生解决方案。由此产生的解决方案是我们如何在奖励中权衡步态正则化的函数，并在速度和本体感受噪声的鲁棒性之间进行权衡。总的来说，我们的结果将大脑-身体-环境三元组嵌入到学习方案中，与软机器人技术相关，同时揭示了运动的进化和发展。