论文标题
用于储层计算的尖峰神经元的种群：符合四足的闭环控制
Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped
论文作者
论文摘要
兼容的机器人比传统机器人更通用，但它们的控制更为复杂。但是，使用物理储层计算框架可以将兼容物体的动态变成优势。通过将传感器信号馈送到储层并从储层中提取电动机信号，可以使用闭环机器人控制。在这里，我们提出了一个新颖的框架，用于实现具有尖峰神经网络的中央模式发生器，以获得闭环机器人控制。使用力量学习范式，我们训练尖峰神经元种群的水库充当中央模式发生器。我们证明了在符合四足动物机器人的模拟模型上的预定步态模式，速度控制和步态过渡的学习。
Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing frame-work. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
