Integrated Concept of Human Motions and Objects based on Multi-layered Multimodal LDA

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  • Attamimi Muhammad
    Faculty of Informatics and Engineering, The University of Electro-Communications
  • Fadlil Muhammad
    Faculty of Informatics and Engineering, The University of Electro-Communications
  • Abe Kasumi
    Faculty of Informatics and Engineering, The University of Electro-Communications
  • Nakamura Tomoaki
    Faculty of Informatics and Engineering, The University of Electro-Communications Honda Research Institute Japan Co., Ltd.
  • Funakoshi Kotaro
    Honda Research Institute Japan Co., Ltd.
  • Nagai Takayuki
    Faculty of Informatics and Engineering, The University of Electro-Communications

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Other Title
  • 多層マルチモーダルLDAを用いた人の動きと物体の統合概念の形成
  • タソウ マルチモーダル LDA オ モチイタ ヒト ノ ウゴキ ト ブッタイ ノ トウゴウ ガイネン ノ ケイセイ

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The human understanding of things is based on prediction which is made through concepts formed by categorization of their experience. To mimic this mechanism in robots, multimodal categorization, which enables the robot to form concepts, has been studied. On the other hand, segmentation and categorization of human motions have also been studied to recognize and predict future motions. This paper addresses the issue of how these concepts are integrated to generate higher level concepts and, more importantly, how the higher level concepts affect each lower level concept formation. To this end, we propose multi-layered multimodal latent Dirichlet allocation (mMLDA) to learn and represent the hierarchical structure of concepts. We also examine a simple integration model and compare with the mMLDA. The experimental results reveal that the mMLDA leads to better inference performance and, indeed, forms higher level concepts integrating motions and objects that are necessary for real-world understanding.

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