Acquiring vocabulary through human robot interaction: A learning architecture for grounding words with multiple meanings

Aneesh Chauhan*, Luís Seabra Lopes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

3 Citations (Scopus)

Abstract

This paper presents a robust methodology for grounding vocabulary in robots. A social language grounding experiment is designed, where, a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. Any system for grounding vocabulary has to incorporate the properties of gradual evolution and lifelong learning. The learning model of the robot is adopted from an ongoing work on developing systems that conform to these properties. Significant modifications have been introduced to the adopted model, especially to handle words with multiple meanings. A novel classification strategy has been developed for improving the performance of each classifier for each learned category. A set of six new nearest-neighbor based classifiers have also been integrated into the agent architecture. A series of experiments were conducted to test the performance of the new model on vocabulary acquisition. The robot was shown to be robust at acquiring vocabulary and has the potential to learn a far greater number of words (with either single or multiple meanings).

Original languageEnglish
Title of host publicationDialog with Robots - Papers from the AAAI Fall Symposium, Technical Report
Pages8-13
Number of pages6
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event2010 AAAI Fall Symposium - Arlington, VA, United States
Duration: 11 Nov 201013 Nov 2010

Publication series

NameAAAI Fall Symposium - Technical Report
VolumeFS-10-05

Conference/symposium

Conference/symposium2010 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA
Period11/11/1013/11/10

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