Towards understanding how humans teach robots

Tasneem Kaochar, Raquel Torres Peralta, Clayton T. Morrison, Ian R. Fasel, Thomas J. Walsh, Paul R. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Our goal is to develop methods for non-experts to teach complex behaviors to autonomous agents (such as robots) by accommodating "natural" forms of human teaching. We built a prototype interface allowing humans to teach a simulated robot a complex task using several techniques and report the results of 44 human participants using this interface. We found that teaching styles varied considerably but can be roughly categorized based on the types of interaction, patterns of testing, and general organization of the lessons given by the teacher. Our study contributes to a better understanding of human teaching patterns and makes specific recommendations for future human-robot interaction systems.

Original languageEnglish (US)
Title of host publicationUser Modeling, Adaption, and Personalization - 19th International Conference, UMAP 2011, Proceedings
Pages347-352
Number of pages6
DOIs
StatePublished - Jul 19 2011
Event19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011 - Girona, Spain
Duration: Jul 11 2011Jul 15 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6787 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011
CountrySpain
CityGirona
Period7/11/117/15/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Kaochar, T., Peralta, R. T., Morrison, C. T., Fasel, I. R., Walsh, T. J., & Cohen, P. R. (2011). Towards understanding how humans teach robots. In User Modeling, Adaption, and Personalization - 19th International Conference, UMAP 2011, Proceedings (pp. 347-352). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6787 LNCS). https://doi.org/10.1007/978-3-642-22362-4_31