Motivation oriented action selection for understanding dynamics of objects

In this study, we propose a synthetic methodology that can enable a humanoid robot to understand the dynamics of objects in a psychological framework. The action selection of the robot is carried out based on a selection probability determined by several internal variables that draws upon the motivation mechanism of human beings. This makes the robot classify its action space by a sensory feedback caused by its own action. The system prioritizes actions that are expected to cause distinguishing sensory patterns. The action selection probability allows the robot to explore unknown spaces for understanding the dynamics of objects in a real environment. In our experiment, we demonstrate the performance of object clustering by multi-modal active sensing using the proposed action selection rule. Furthermore, we show that the system allows the robot to build knowledge about common object movements by repeating interactions with several different types of objects, and also to predict the movement of unknown objects.