Learning of exploratory behaviors for object recognition using reinforcement learning

Abstract

In this study, we propose a reinforcement learning method for discernment behaviors of robot. Discernment behavior, which is a type of exploratory behaviors that support object feature extraction, is a fundamental tool for a robot to orientate itself, operate objects and establish higher classes of knowledge. In this method, a robot learns the discernment behaviors through the interaction with multiple objects. While the interaction, the robot takes reinforcement signal according to the cluster distance of the observed data. We validated the effectiveness of the model in a mobile robot simulation. Three different shaped objects were placed beside the robot one by one. In this learning, the robot learned different behaviors corresponding to each object. Then, we confirmed the kind of feature that is extracted from an object using learned exploratory behaviors.