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On-Line Learning of Lexical Items and Grammatical Constructions via Speech, Gaze and Action-Based Human-Robot Interaction

Abstract : In order to be able to understand a conversation in interaction, a robot, has to first understand the language used by his interlocutor. A central aspect of language learning is adaptability. Individuals can learn new words and new grammatical structures. We have developed learning methods that allow the hu-manoid robot iCub to robot can learn new lexical items by interaction with the human and consolidation of its autobiographical memory. Then, based on these open class words, the robot can bootstrap the acquisition of novel grammatical structures in real-time. Finally, we demonstrate how human gaze can be monitored, and could be used in order to reduce referential ambiguity inherent in such learning conditions. These learning capabilities are demonstrated in a collection of videos.
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Submitted on : Sunday, May 3, 2020 - 6:48:08 PM
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Gregoire Pointeau, Maxime Petit, Xavier Hinaut, Guillaume Gibert, Peter Dominey. On-Line Learning of Lexical Items and Grammatical Constructions via Speech, Gaze and Action-Based Human-Robot Interaction. INTERSPEECH 2013 - 14th Annual Conference of the International Speech Communication Association, Aug 2013, Lyon, France. ⟨hal-02561340⟩

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