What Am I Allowed to Do Here? Online Learning of Context-Specific Norms by Pepper

Ali Ayub, Alan R. Wagner

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

Abstract

Social norms support coordination and cooperation in society. With social robots becoming increasingly involved in our society, they also need to follow the social norms of the society. This paper presents a computational framework for learning contexts and the social norms present in a context in an online manner on a robot. The paper utilizes a recent state-of-the-art approach for incremental learning and adapts it for online learning of scenes (contexts). The paper further utilizes Dempster-Schafer theory to model context-specific norms. After learning the scenes (contexts), we use active learning to learn related norms. We test our approach on the Pepper robot by taking it through different scene locations. Our results show that Pepper can learn different scenes and related norms simply by communicating with a human partner in an online manner.

Original languageEnglish (US)
Title of host publicationSocial Robotics - 12th International Conference, ICSR 2020, Proceedings
EditorsAlan R. Wagner, David Feil-Seifer, Kerstin S. Haring, Silvia Rossi, Thomas Williams, Hongsheng He, Shuzhi Sam Ge
PublisherSpringer Science and Business Media Deutschland GmbH
Pages220-231
Number of pages12
ISBN (Print)9783030620554
DOIs
StatePublished - 2020
Event12th International Conference on Social Robotics, ICSR 2020 - Golden, United States
Duration: Nov 14 2020Nov 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12483 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Social Robotics, ICSR 2020
CountryUnited States
CityGolden
Period11/14/2011/18/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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