Efficient Spatial Concept Formation by Active Exploration of the Environment

Abstract

Autonomous service robots are required to adaptively learn the categories and names of various places through the exploration of the surrounding environment and interactions with users. In this study, we aim to realize the efficient learning of spatial concepts by autonomous active exploration with a mobile robot. Therefore, we propose an active learning algorithm that combines sequential Bayesian inference by a particle filter and position determination based on information-gain in probabilistic generative models. Our experiment shows that the proposed method can efficiently determine the position to form spatial concepts in simulated home environments.