複数物体が存在する環境下での共同注意を用いたロボットによる語意学習

抄録

Humans can learn word meanings by associating objects with words even in an environment with a plurality of objects by using joint attention, which is an ability to detect a target object that others pay attention to. In this paper, we propose a method for robots to learn word meanings using joint attention and co-occurrence of objects and words, which is modeled by multimodal latent Dirichlet allocation (MLDA). A target object is detected by using MLDA and joint attention, and MLDA is updated by the detected object. This updated MLDA can improve the accuracy of the target object detection.