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In this paper, we propose an online algorithm for multimodal categorization based on the autonomously acquired multimodal information and partial words given by human users. For multimodal concept formation, multimodal latent Dirichlet allocation (MLDA) using Gibbs sampling is extended to an online version. We introduce a particle filter, which significantly improve the performance of the online MLDA,...
This paper proposes a robot that acquires multi-modal information, i.e. auditory, visual, and haptic information, fully autonomous way using its embodiment. We also propose an online algorithm of multimodal categorization based on the acquired multimodal information and words, which are partially given by human users. The proposed framework makes it possible for the robot to learn object concepts...
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