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A probabilistic framework for comparing syntactic and semantic grounding of synonyms through cross-situational learning

Roesler, O., Aly, A., Taniguchi, T. and Hayashi, Y. (2018) A probabilistic framework for comparing syntactic and semantic grounding of synonyms through cross-situational learning. In: ICRA 2018 'Representing a Complex World: Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding', 21-25 May 2018, Brisbane, Australia. (In Press)

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Abstract/Summary

Abstract— Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, none of them considered grounding of synonyms. In this paper, we try to fill this gap by introducing a Bayesian learning model for grounding synonymous object and action names using cross- situational learning. Three different word representations are employed with the probabilistic model and evaluated according to their grounding performance. Words are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. An interaction experiment between a human tutor and HSR robot is used to evaluate the proposed model. The results show that representing words by syntactic and/or semantic information achieves worse grounding results than representing them by unique numbers.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:77209

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