Towards an Affect Space for robots to display emotional body language

Abstract

Original article can be found at: http://ieeexplore.ieee.org ???This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." ???Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.???In order for robots to be socially accepted and generate empathy it is necessary that they display rich emotions. For robots such as Nao, body language is the best medium available given their inability to convey facial expressions. Displaying emotional body language that can be interpreted whilst interacting with the robot should significantly improve its sociability. This research investigates the creation of an Affect Space for the generation of emotional body language to be displayed by robots. To create an Affect Space for body language, one has to establish the contribution of the different positions of the joints to the emotional expression. The experiment reported in this paper investigated the effect of varying a robot's head position on the interpretation, Valence, Arousal and Stance of emotional key poses. It was found that participants were better than chance level in interpreting the key poses. This finding confirms that body language is an appropriate medium for robot to express emotions. Moreover, the results of this study support the conclusion that Head Position is an important body posture variable. Head Position up increased correct identification for some emotion displays (pride, happiness, and excitement), whereas Head Position down increased correct identification for other displays (anger, sadness). Fear, however, was identified well regardless of Head Position. Head up was always evaluated as more highly Aroused than Head straight or down. Evaluations of Valence (degree of negativity to positivity) and Stance (degree to which the robot was aversive to approaching), however, depended on both Head Position and the emotion displayed. The effects of varying this single body posture variable were complex

Towards Long-Term Social Child-Robot Interaction: Using Multi-Activity Switching to Engage Young Users

“…Affective communication has also been shown to provide additional facets to robot behaviour, making the relationship between robot and child deeper and richer. For example, emotional pose recognition with the Nao robot is consistent (Beck, Canamero, & Bard, 2010), facilitating the use of emotional expression as a feedback channel that can be employed by the robot. Following from this general competence, it has been shown that an empathic or supportive robot is prefered to a non-affective agent by children, and that its use can possibly lead to increased performance, in both teaching scenarios (Saerbeck, Schut, Bartneck, & Janse, 2010), and peer-peer interactions (Leite, Castellano, Pereira, Martinho, & Paiva, 2012).…”

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“…automatic communicative gestures) are evoked to accompany specific types of dialogue 4 project website http://aliz-e.org 5 http://www.aldebaran-robotics.com 6 http://opencv.willowgarage.com/wiki/ 7 http://www.acapela-group.com/ 8 http://openccg.sourceforge.net/ 9 http://www.cs.waikato.ac.nz/ml/weka/ 10 http://www.cs.cmu.edu/ ∼ javabayes/Home/ 11 Features for the Bayes net dialogue act recogniser: lastSystemDialogueAct, subtask, hasName, hasYes, hasNo, hasCorrect, hasIncorrect, hasRepeat, hasStop, hasContinue, hasAskMe, hasAskYou, hasAnswer, hasAnswers, hasIDontKnow, hasQuestion; where the first two are non-binary features and the rest are binary ones filled from speech recognition N-best hypotheses. acts (e.g., greetings, requests) as well as static key poses that display emotions such as anger, sadness, fear, happiness, excitement and pride [Beck et al 2010]. The following features summarize the capabilities of the interactive conversational robot used in our experiments:…”