论文标题
独立于任务的尖峰中心模式生成器：一种基于学习的方法
Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach
论文作者
论文摘要
在机器人技术领域，腿部运动是一项具有挑战性的任务，但本质上是一个简单的任务。这激发了将生物学方法论用作解决这个问题的解决方案。中央模式发生器是神经网络，被认为是人类和某些动物物种的运动。至于机器人技术，进行了许多尝试复制此类系统并将其用于类似目标的尝试。一个有趣的设计模型是基于尖峰神经网络。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，而且适用于神经科学。本文介绍了一个新的通用框架，用于构建与任务无关，生物学合理并依赖学习方法的中心模式发生器。提出的方法的能力和特性不仅在模拟中评估，而且在机器人实验中进行了评估。结果非常有前途，因为二手机器人能够以不同的速度进行稳定行走，并在同一步态周期内改变速度。
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.
