Online exploratory behavior acquisition model based on reinforcement learning

Abstract

Discernment behavior is an exploratory behavior that supports object feature extraction and is a fundamental tool used by robots to orient themselves, operate objects, and establish knowledge. The main contribution of this paper is to propose an active perception model and analyzes the acquired motion patterns. In this study, we propose an active perception model in which a robot autonomously learns discernment behavior by interacting with multiple objects in its environment. During such interactions, the robot receives reinforcement signals according to the cluster distance of the observed data. In other words, we use a reinforcement learning approach to reward the successful recognition of objects. We apply our proposed model to a mobile robot simulation to observe its effectiveness. Results show that our proposed model effectively established intelligent strategies based on the relationship between object features and the robot's configuration. In addition, we perform our experiments using real mobile robots and observe the suitability of the observed learned behaviors.