Online Exploratory Behavior Acquisition of Mobile Robot Based on Reinforcement Learning

In this study, we propose an online active perception system that autonomously acquires exploratory behaviors suitable for each embodiment of mobile robots using online learning. We especially focus on a type of exploratory behavior that extracts object features useful for robot’s orientation and object operation. The proposed system is composed of a classification system and a reinforcement learning system. While a robot is interacting with objects, the classification system classifies observed data and calculates reward values according to the cluster distance of the observed data. On the other hand, the reinforcement learning system acquires effective exploratory behaviors useful for the classification according to the reward. We validated the effectiveness of the system 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. The result showed that the behaviors were the exploratory behaviors that distinguish the difference of corner angles of the objects.