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A Cross-Situational Learning Based Framework for Grounding of Synonyms in Human-Robot Interactions

  • Oliver RoeslerEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1093)

Abstract

Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, not many considered grounding of synonyms and the majority of employed models only worked offline. In this paper, we try to fill this gap by introducing an online learning framework for grounding synonymous object and action names using cross-situational learning. 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 framework. The results show that the employed framework is able to successfully ground all used words.

Keywords

Language grounding Cross-situational learning Human-robot interaction 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Artificial Intelligence LabVrije Universiteit BrusselBrusselsBelgium

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