Categorization in a real-world agent using haptic exploration and active perception

An agent in the real world has to be able to make distinctions between different types of objects, i.e. it must have the competence of categorization. In mobile agents categorization is hard to achieve because there is a large variation in proximal sensory stimulation originating from the same object. In this paper we extend previous work on adaptive categorization in autonomous agents. The main idea of our approach is to include the agent's own actions into the classification process. In the experiments presented in this paper an agent equipped with an active vision and an arm-gripper system has to collect certain types of objects. The agent learns about the objects by actively exploring them. This exploration results in visual and haptic information that is used for learning. In essence, categorization comes about via evolving reentrant connections between the haptic and the visual system. Results on the behavioral performance as well as the underlying internal dynamics are presented.