Bag of multimodal hierarchical dirichlet processes: Model of complex conceptual structure for intelligent robots

Tomoaki Nakamura, T. Nagai, N. Iwahashi
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引用次数: 8

Abstract

The formation of categories, which constitutes the basis of developing concepts, requires multimodal information with a complex structure. We propose a model called the bag of multimodal hierarchical Dirichlet processes (BoMHDP), which enables robots to form a variety of multimodal categories. The BoMHDP model is a collection of a large number of MHDP models, each of which has a different set of weights for sensory information. The weights work to realize selective attention and enable the formation of various types of categories (e.g., object, haptic, and color). The BoMHDP model is an extension of the HDP, and categorization is unsupervised. However, categories that are not natural for humans are also formed. Therefore, only the significant categories are selected through interaction between the user and the robot. At the same time, words obtained during the interaction are connected to the categories. Finally, categories, which are represented by words, are selected. The BoMHDP model was implemented on a robot platform and a preliminary experiment was conducted to validate it. The results revealed that various categories can be formed with the BoMHDP model. We also analyzed the formed conceptual structure by using multidimensional scaling. The results indicate that the complex conceptual structure was represented reasonably well with the BoMHDP model.
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多模态分层狄利克雷过程包:智能机器人复杂概念结构模型
范畴的形成是概念发展的基础,它需要具有复杂结构的多模态信息。我们提出了一个多模态分层狄利克雷过程包模型(BoMHDP),该模型使机器人能够形成多种多模态类别。BoMHDP模型是大量MHDP模型的集合,每个MHDP模型都有一组不同的感官信息权重。权重的作用是实现选择性注意,并形成各种类型的类别(例如,物体、触觉和颜色)。BoMHDP模型是HDP的扩展,分类是无监督的。然而,对人类来说不自然的分类也形成了。因此,通过用户和机器人之间的交互,只选择有意义的类别。同时,在交互过程中获得的词被连接到类别中。最后,选择用单词表示的类别。在机器人平台上实现了BoMHDP模型,并进行了初步实验验证。结果表明,BoMHDP模型可以形成不同的类别。我们还利用多维尺度分析了所形成的概念结构。结果表明,BoMHDP模型能较好地表征复杂的概念结构。
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