This paper presents a machine learning method that enables robots to learn to communicate linguistically from scratch through verbal and behavioral interaction with users. The method combines speech, visual, and tacti...
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This paper presents a machine learning method that enables robots to learn to communicate linguistically from scratch through verbal and behavioral interaction with users. The method combines speech, visual, and tactile information ob- tained by interaction in the real world. It learns speech units, words, concepts of objects, motions, grammar, and pragmatic and communicative capabilities, which are integrated in a dy- namic graphical model. Experimental results show that through a practical, small number of learning episodes with a user, the robot was eventually able to understand even fragmental and ambiguous utterances, respond to them with confirmation ques- tions and/or acting, generate directive utterances appropriate for the given situation, and answer questions. This paper discusses the importance of a developmentalapproach to realize natural situated human-robot conversations.
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