Large-Scale Performance Evaluation Experiments on Name and Location Prediction of Spatial Concept Transfer Leaning Model

Abstract

Spatial concept transfer learning model was first for the purpose of transferring the knowledge of places acquired in learning environments when the robot moves to new environments. However, in previous studies, this model has not proven to be effective for transferring the knowledge of places to new environments. Therefore, in this paper, we conduct large-scale performance evaluation experiments on name and localization prediction of this model in new environments and we verify whether this model is effective for transferring knowledge of places to a new environment. The experiment results on a larger scale showed that the model has a effectively a high prediction performance of name and location in new environments, and can indeed transfer the knowledge of prior places.