Legged locomotion is a challenging task in the field of robotics but a rather simple one in nature. This motivates the use of biological methodologies as solutions to this problem. Central pattern generators are neural networks that are thought to be responsible for locomotion in humans and some animal species. As for robotics, many attempts were made to reproduce such systems and use them for a similar goal. One interesting design model is based on spiking neural networks. This model is the main focus of this work, as its contribution is not limited to engineering but also applicable to neuroscience. This paper introduces a new general framework for building central pattern generators that are task-independent, biologically plausible, and rely on learning methods. The abilities and properties of the presented approach are not only evaluated in simulation but also in a robotic experiment. The results are very promising as the used robot was able to perform stable walking at different speeds and to change speed within the same gait cycle.

中文翻译：

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任务无关尖峰中央模式生成器：一种基于学习的方法

腿部运动在机器人领域是一项具有挑战性的任务，但本质上是一项相当简单的任务。这激发了使用生物学方法作为解决这个问题的方法。中央模式生成器是神经网络，被认为负责人类和某些动物物种的运动。至于机器人技术，人们进行了许多尝试来复制这样的系统并将它们用于类似的目标。一种有趣的设计模型基于尖峰神经网络。该模型是这项工作的主要重点，因为它的贡献不仅限于工程，还适用于神经科学。本文介绍了一种新的通用框架，用于构建独立于任务、生物学上合理且依赖于学习方法的中央模式生成器。所提出方法的能力和特性不仅在模拟中进行评估，而且在机器人实验中进行评估。结果非常有希望，因为所使用的机器人能够以不同的速度执行稳定的步行，并在相同的步态周期内改变速度。