Continual Learning for Addressing Optimization Problems with a Snake-Like Robot Controlled by a Self-Organizing Model

Abstract

We have entered a new era, “Industry 4.0”, that sees the overall industry marching toward an epoch of man–machine symbiosis and intelligent production. The developers of so-called “intelligent” systems must attempt to seriously take into account all possible situations that might occur in the real world, to minimize unexpected errors. By contrast, biological systems possess comparatively better “adaptability” than man-made machines, as they possess a self-organizing learning that plays an indispensable role. The objective of this study was to apply a malleable learning system to the movement control of a snake-like robot, to investigate issues related to self-organizing dynamics. An artificial neuromolecular (ANM) system previously developed in our laboratory was used to control the movements of an eight-joint snake-like robot (called Snaky). The neuromolecular model is a multilevel neural network that abstracts biological structure–function relationships into the system’s structure, in particular into its intraneuronal structure. With this feature, the system possesses structure richness in generating a broad range of dynamics that allows it to learn how to complete the assigned tasks in a self-organizing manner. The activation and rotation angle of each motor are dependent on the firing activity of neurons that control the motor. An evolutionary learning algorithm is used to train the system to complete the assigned tasks. The key issues addressed include the self-organizing learning capability of the ANM system in a physical environment. The experimental results show that Snaky was capable of learning in a continuous manner. We also examined how the ANM system controlled the angle of each of Snaky’s joints, to complete each assigned task. The result might provide us with another dimension of information on how to design the movement of a snake-like robot.