Evolutionary Creation of Adaptive Neurocontrollers Inspired by the Dynamically Rearranging Function in Biological Nervous Systems

近年来，进化机器人（ER）方法在机器人和人工生命领域引起了广泛关注。与设计人员必须以自上而下的方式构建控制器的传统方法相比，ER方法中采用的方法具有显着的优势，因为它们可以通过考虑实施例（例如机械动力学，机器人的物理尺寸和形状，传感器/电机特性和配置等）以及机器人与其环境之间的交互动力学来自主有效地构建控制器。鉴于这些事实，在本研究项目中，研究了自适应神经控制器的进化创造。为此，引入了神经调节的概念，允许进化神经网络，它不仅可以根据当前情况通过阻断和/或激活突触或神经元来调节突触的权重，还可以调节神经网络的结构。该思想已被应用于推钉机器人和有腿机器人的控制中，这些机器人本质上是不稳定的。已经进行了模拟，结果表明这种方法非常有前途。

Recently the Evolutionary Robotics (ER) approach has been attracting a lot of concern in the field of robotics and artificial life. In contrast to the conventional approaches where designers have to construct controllers in a top-down manner, the methods employed in the ER approach have significant advantages since they can autonomously and efficiently construct controllers by taking embodiment (e.g. mechanical dynamics, physical size and shape of robots, sensor/motor properties and disposition etc.) and the interaction dynamics between the robot and its environment into account.In light of these facts, in this research project, an evolutionary creation of adaptive neurocontrollers has been investigated. To this end, the concept of neuromodulation, allowing to evolve neural networks which can adjust not only the synaptic weights but also the structure of the neural network by blocking and/or activating synapses or neurons according to the current situation has been introduced. This idea has been applied to the control of a peg-pushing robot and a legged robot, which is intrinsically unstable. Simulations have been conducted and the results have showed that this approach is remarkably promising.