Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information
This work addresses the challenge of knowledge transfer for robots in spatial navigation, though it appears incremental as it builds on existing Bayesian models for spatial concepts.
The paper tackles the problem of enabling robots to transfer spatial knowledge from experienced environments to new ones using a hierarchical Bayesian model, achieving higher prediction accuracy for location names and positions compared to conventional methods.
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.