An Extended Bayesian User Model (BUM) for Capturing Cultural Attributes with a Social Robot

Luis Santos, Goncalo S. Martins, Jorge Dias

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

Abstract

In this work we propose a Bayesian User Model which is able capture a unified representation of cultural attributes from heterogeneous information in the context of Human-Robot Interaction. Despite the latest advances in robotic technologies, virtually no robots are able to cope with the specificities of the 'modus vivendi' of different cultures. We start by proposing Bayesian classifiers to capture unitary attributes of different users, clustering them in a n-dimensional semantic attribute space, aggregating groups of persons that share similar attributes. Results show a highly accurate classification framework, both capable of detecting specific subtleties in user's properties, and generalizing them into representative profiles. We then discuss its application towards adapting the actions of a robot and its potential impact on culture-awareness, demonstrating how the proposed framework can enable culture-awareness, exploring this new frontier in social robotics.

Original languageBritish English
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages320-325
Number of pages6
ISBN (Electronic)9781538680940
DOIs
StatePublished - 27 Dec 2018
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018
Country/TerritorySpain
CityMadrid
Period1/10/185/10/18

Keywords

  • Culture Aware Social Robots
  • Multimodal Human-Robot Interaction
  • Robot Perception
  • User Models

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