On the dynamics of robot exploration learning

Abstract

In this paper, the processes of exploration and of incremental learning in the robot navigation task are studied using the dynamical systems approach. A neural network model which performs the forward modeling, planning, consolidation learning and novelty rewarding is used for the robot experiments. Our experiments showed that the robot repeated a few variation of travel patterns in the beginning of the exploration, and later the robot explored more diversely in the workspace by combining and mutating the previously experienced patterns. Our analysis indicates that internal confusion due to immature learning plays the role of a catalyst in generating diverse action sequences. It is found that these diverse exploratory travels enable the robot to acquire the rational modeling of the environment in the end.