Emotion-expression robot with global evaluation reflecting multiple and heterogeneous sensing elements

Yoshiko Itabashi, Yasushi Kiyoki

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

Abstract

A new generation robot for communication is expected to have various abilities related to changing the expression of emotion. There are many factors related to human sensitivities that affect emotional expression, and their relationships are complicated. Moreover, emotional expression should change according to the purpose of the application. In order to realize programmable emotional expression, we proposed a method using global evaluation for the heterogeneous sensing elements in a Kansei space for expression. The important feature of our system was that it realized an emotion-expressing robot with sentence-unit-based emotion analysis. Various emotional expressions were generated from the results of evaluation. We conducted verification experiments and obtained good evaluations of emotional expression changes and the ease of making settings.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXX
EditorsTatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
PublisherIOS Press
Pages369-383
Number of pages15
ISBN (Electronic)9781614999324
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume312
ISSN (Print)0922-6389

Keywords

  • Affect Computing
  • Emotion
  • Emotional Expression
  • Kansei
  • Robotics

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Itabashi, Y., & Kiyoki, Y. (2019). Emotion-expression robot with global evaluation reflecting multiple and heterogeneous sensing elements. In T. Endrjukaite, H. Jaakkola, A. Dudko, Y. Kiyoki, B. Thalheim, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXX (pp. 369-383). (Frontiers in Artificial Intelligence and Applications; Vol. 312). IOS Press. https://doi.org/10.3233/978-1-61499-933-1-369