A design for smooth transition of robotic emotional states

Meng Ju Han*, Chia How Lin, Kai-Tai Song

*Corresponding author for this work

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

1 Scopus citations

Abstract

A two-dimensional (2-D) emotional model is proposed to represent the emotional state of a sociable robot. We present a novel design of autonomous robotic emotional state transition based on a fuzzy-neuro network. By using fuzzy Kohonen clustering network (FKCN), a smooth transition of the robotic emotional states is obtained to generate continuous emotional behaviors. Moreover, the robotic emotional character can be specified in this design by assigning weights to the FKCN. In this study, four emotional characters and five primitive emotional behaviors (boring, smiling, crying, angry and relaxed) have been designed for a robotic face. The method has been tested by a graphical simulator to verify the robotic responses to the user's emotional intensity (neutral, happiness, anger and sadness). Computer simulations show that the proposed emotional state transition scheme effectively responds to user's emotional intensity in a continuous manner. Practical experiments on a 16-DOF robotic face validate the proposed design.

Original languageEnglish
Title of host publication2010 IEEE Workshop on Advanced Robotics and Its Social Impacts, ARSO 2010 - Conference Proceedings
Pages13-18
Number of pages6
DOIs
StatePublished - 1 Dec 2010
Event2010 IEEE Workshop on Advanced Robotics and Its Social Impacts, ARSO 2010 - Seoul, Korea, Republic of
Duration: 26 Oct 201028 Oct 2010

Publication series

NameProceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO
ISSN (Print)2162-7568
ISSN (Electronic)2162-7576

Conference

Conference2010 IEEE Workshop on Advanced Robotics and Its Social Impacts, ARSO 2010
CountryKorea, Republic of
CitySeoul
Period26/10/1028/10/10

Fingerprint Dive into the research topics of 'A design for smooth transition of robotic emotional states'. Together they form a unique fingerprint.

Cite this