基于强化学习的协同爬行
Coordinated Crawling via Reinforcement Learning
摘要：
直线爬行运动是细长软体动物的一种原始而常见的运动方式。它需要协调的收缩，沿着与环境相互作用的身体传播。我们提出了一个简单的方法来了解这些协调如何出现在神经力学模型的分段，软体爬行动物通过迭代过程，可能有生物的前因和技术相关性。使用一个简单的强化学习算法，我们证明了一个初始的全对全神经耦合收敛到一个简单的最近邻神经布线，允许爬行器使用一个局部的收缩波向前移动，这在定性上与D中观察到的相似。黑腹幼虫，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加权步态正则化的函数，在速度和对本体感受噪声的鲁棒性之间进行权衡。总的来说，我们的研究结果将脑-体-环境三元组嵌入到学习方案中，与软机器人相关，同时揭示了运动的进化和发展。
英文摘要：
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.
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