Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation

Autonomous robots are required to actively and adaptively learn the categories and words of various places by exploring the surrounding environment and interacting with users. In semantic mapping and spatial language acquisition conducted using robots, it is costly and labor-intensive to prepare training datasets that contain linguistic instructions from users. Therefore, we aimed to enable mobile robots to learn spatial concepts through autonomous active exploration. This study is characterized by interpreting the `action' of the robot that asks the user the question `What kind of place is this?' in the context of active inference. We propose an active inference method, spatial concept formation with information gain-based active exploration (SpCoAE), that combines sequential Bayesian inference by particle filters and position determination based on information gain in a probabilistic generative model. Our experiment shows that the proposed method can efficiently determine a position to form appropriate spatial concepts in home environments. In particular, it is important to conduct efficient exploration that leads to appropriate concept formation and quickly covers the environment without adopting a haphazard exploration strategy.