NDLI: Integrating whole body motion primitives and natural language for humanoid robots

Content Provider	IEEE Xplore Digital Library
Author	Takano, W. Nakamura, Y.
Copyright Year	2008
Description	Author affiliation: Univ. of Tokyo, Tokyo (Takano, W.; Nakamura, Y.)
Abstract	This paper describes the novel approach to integration of natural language processing and symbolization of motion patterns in order to allow for humanoid robotpsilas acquisition of language. This framework consists of two models : motion language model and natural language model. In the motion language model, morpheme words are stochastically associated with symbolized motion patterns via latent variables. The association is defined by probability that the motion pattern generates the latent variable and probability that the latent variable generates the morpheme word. The natural language model represents order relation among the morpheme words via word classes by using hidden Markov model. The motion language model and the natural language model are equivalent to semantics and syntax respectively. Integration of the motion language model and the natural language model achieves linguistic interpretation of motion patterns by composing semantically and syntactically appropriate sentence. The efficient algorithm for the composition is proposed. The validity of the motion language model, the natural language model and the integration is demonstrated by testing the implemented algorithm on human motion data.
Starting Page	708
Ending Page	713
File Size	2506083
Page Count	6
File Format	PDF
ISBN	9781424428212
DOI	10.1109/ICHR.2008.4755976
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural languages Neurons Humanoid robots Humans Hidden Markov models Natural language processing Pattern recognition Mirrors Intelligent robots Testing
Content Type	Text
Resource Type	Article

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