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Developmental Word Acquisition and Grammar Learning by Humanoid Robots Through a Self-Organizing Incremental Neural Network

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4 Author(s)
Xiaoyuan He ; Tokyo Inst. of Technol., Yokohama ; Ogura, T. ; Satou, A. ; Hasegawa, O.

We present a new approach for online incremental word acquisition and grammar learning by humanoid robots. Using no data set provided in advance, the proposed system grounds language in a physical context, as mediated by its perceptual capacities. It is carried out using show-and-tell procedures, interacting with its human partner. Moreover, this procedure is open-ended for new words and multiword utterances. These facilities are supported by a self-organizing incremental neural network, which can execute online unsupervised classification and topology learning. Embodied with a mental imagery, the system also learns by both top-down and bottom-up processes, which are the syntactic structures that are contained in utterances. Thereby, it performs simple grammar learning. Under such a multimodal scheme, the robot is able to describe online a given physical context (both static and dynamic) through natural language expressions. It can also perform actions through verbal interactions with its human partner.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:37 ,  Issue: 5 )

Date of Publication:

Oct. 2007

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