S. Boucenna, P. Gaussier, P. Andry, L. Hafemeister
{"title":"Imitation as a communication tool for online facial expression learning and recognition","authors":"S. Boucenna, P. Gaussier, P. Andry, L. Hafemeister","doi":"10.1109/IROS.2010.5650357","DOIUrl":null,"url":null,"abstract":"We are interested in understanding how babies learn to recognize facial expressions without having a teaching signal allowing to associate a facial expression to a given abstract label (i.e the name of the facial expression ‘sadness’, ‘happiness’…). Our starting point was a mathematical model showing that if the baby uses a sensory motor architecture for the recognition of the facial expression then the parents must imitate the baby facial expression to allow the on-line learning. In this paper, a first series of robotics experiments showing that a simple neural network model can control the robot head and learn on-line to recognize the facial expressions (the human partner imitates the robot prototypical facial expressions) is presented. We emphasize the importance of the emotions as a mechanism to ensure the dynamical coupling between individuals allowing to learn more complex tasks","PeriodicalId":420658,"journal":{"name":"2010 IEEE/RSJ International Conference on Intelligent Robots and Systems","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE/RSJ International Conference on Intelligent Robots and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IROS.2010.5650357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33
Abstract
We are interested in understanding how babies learn to recognize facial expressions without having a teaching signal allowing to associate a facial expression to a given abstract label (i.e the name of the facial expression ‘sadness’, ‘happiness’…). Our starting point was a mathematical model showing that if the baby uses a sensory motor architecture for the recognition of the facial expression then the parents must imitate the baby facial expression to allow the on-line learning. In this paper, a first series of robotics experiments showing that a simple neural network model can control the robot head and learn on-line to recognize the facial expressions (the human partner imitates the robot prototypical facial expressions) is presented. We emphasize the importance of the emotions as a mechanism to ensure the dynamical coupling between individuals allowing to learn more complex tasks