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Online Algorithm for Robots to Learn Object Concepts and Language Model
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DOI：10.1109/TCDS.2016.2552579.png)
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摘要
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Humans form concept of objects by classifying them into categories, and acquire language by simultaneously interacting with others. Thus, the meaning of a word can be learned by connecting a recognized word to its corresponding concept. We consider this ability important for robots to flexibly develop knowledge of language and concepts. In this paper, we propose an online algorithm for robots to acquire knowledge of natural language and learn object concepts. A robot learns the language model from word sequences, which are obtained by the segmentation of phoneme sequences provided by a user, by using unsupervised word segmentation each time it is provided with a new object. Moreover, the robot acquires object concepts using these word sequences as well as multimodal information obtained by observing objects. The crucial aspect of our model is the interdependence of words and concepts: there is a high probability that the same words will be uttered to describe objects in the same category. By taking this relationship into account, our proposed method enables robots to acquire a more accurate language model and object concepts online. Experimental results verify this.
Keyword:
Language acquisition
multimodal categorization
multimodal latent Dirichlet allocation (MLDA)
object concepts
online learning
unsupervised learning
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