  This paper proposes a hierarchical Bayesian model based on spatial concepts
that enables a robot to transfer the knowledge of places from experienced
environments to a new environment. The transfer of knowledge based on spatial
concepts is modeled as the calculation process of the posterior distribution
based on the observations obtained in each environment with the parameters of
spatial concepts generalized to environments as prior knowledge. We conducted
experiments to evaluate the generalization performance of spatial knowledge for
general places such as kitchens and the adaptive performance of spatial
knowledge for unique places such as `Emma's room' in a new environment. In the
experiments, the accuracies of the proposed method and conventional methods
were compared in the prediction task of location names from an image and a
position, and the prediction task of positions from a location name. The
experimental results demonstrated that the proposed method has a higher
prediction accuracy of location names and positions than the conventional
method owing to the transfer of knowledge.
