Grounding of Word Meanings in Latent Dirichlet Allocation-Based Multimodal Concepts

Abstract

In this paper we propose a latent Dirichlet allocation (LDA)-based framework for multimodal categorization and words grounding by robots. The robot uses its physical embodiment to grasp and observe an object from various view points, as well as to listen to the sound during the observing period. This multimodal information is used for categorizing and forming multimodal concepts using multimodal LDA. At the same time, the words acquired during the observing period are connected to the related concepts, which are represented by the multimodal LDA. We also provide a relevance measure that encodes the degree of connection between words and modalities. The proposed algorithm is implemented on a robot platform and some experiments are carried out to evaluate the algorithm. We also demonstrate simple conversation between a user and the robot based on the learned model.