  Autonomous robots need to learn the categories of various places by exploring
their environments and interacting with users. However, preparing training
datasets with linguistic instructions from users is time-consuming and
labor-intensive. Moreover, effective exploration is essential for appropriate
concept formation and rapid environmental coverage. To address this issue, we
propose an active inference method, referred to as spatial concept formation
with information gain-based active exploration (SpCoAE) that combines
sequential Bayesian inference using particle filters and information gain-based
destination determination in a probabilistic generative model. This study
interprets the robot's action as a selection of destinations to ask the user,
`What kind of place is this?' in the context of active inference. This study
provides insights into the technical aspects of the proposed method, including
active perception and exploration by the robot, and how the method can enable
mobile robots to learn spatial concepts through active exploration. Our
experiment demonstrated the effectiveness of the SpCoAE in efficiently
determining a destination for learning appropriate spatial concepts in home
environments.
