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