<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aryel Beck</style></author><author><style face="normal" font="default" size="100%">Antoine Hiolle</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using Perlin Noise to Generate Emotional Expressions in a Robot</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Annual Meeting of the Cognitive Science Society (CogSci 2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://mindmodeling.org/cogsci2013/papers/0343/index.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Cognitive Science Society</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin, Germany</style></pub-location><pages><style face="normal" font="default" size="100%">1845–1850</style></pages><isbn><style face="normal" font="default" size="100%">978-0-9768318 -9-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The development of social robots that convey emotion with their bodies---instead of or in conjunction with their faces---is an increasingly active research topic in the field of human-robot interaction (HRI). Rather than focusing either on postural or on dynamics aspects of bodily expression in isolation, we present a model and an empirical study where we combine both elements and produce expressive behaviors by adding dynamic elements (in the form of Perlin noise) to a subset of static postures prototypical of basic emotions, with the aim of creating expressions easily understandable by children and at the same time lively and flexible enough to be believable and engaging. Results show that the noise increases the recognition rate of the emotions portrayed by the robot.</style></abstract><notes><style face="normal" font="default" size="100%">&lt;a href=&quot;https://mindmodeling.org/cogsci2013/papers/0343/index.html&quot;&gt;Download&lt;/a&gt; (Open Access)</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antoine Hiolle</style></author><author><style face="normal" font="default" size="100%">Lola Cañamero</style></author><author><style face="normal" font="default" size="100%">Peirre Andry</style></author><author><style face="normal" font="default" size="100%">Arnaud J Blanchard</style></author><author><style face="normal" font="default" size="100%">Philippe Gaussier</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Shuzhi Sam Ge</style></author><author><style face="normal" font="default" size="100%">Haizhou Li</style></author><author><style face="normal" font="default" size="100%">John-John Cabibihan</style></author><author><style face="normal" font="default" size="100%">Yeow Kee Tan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Using the Interaction Rhythm as a Natural Reinforcement Signal for Social Robots: A Matter of Belief</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. International Conference on Social Robotics, ICSR 2010</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Singapore</style></pub-location><volume><style face="normal" font="default" size="100%">6414</style></volume><pages><style face="normal" font="default" size="100%">81–89</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-17247-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we present the results of a pilot study of a human robot interaction experiment where the rhythm of the interaction is used as a reinforcement signal to learn sensorimotor associations. The algorithm uses breaks and variations in the rhythm at which the human is producing actions. The concept is based on the hypothesis that a constant rhythm is an intrinsic property of a positive interaction whereas a break reflects a negative event. Subjects from various backgrounds interacted with a NAO robot where they had to teach the robot to mirror their actions by learning the correct sensorimotor associations. The results show that in order for the rhythm to be a useful reinforcement signal, the subjects have to be convinced that the robot is an agent with which they can act naturally, using their voice and facial expressions as cues to help it understand the correct behaviour to learn. When the subjects do behave naturally, the rhythm and its variations truly reflects how well the interaction is going and helps the robot learn efficiently. These results mean that non-expert users can interact naturally and fruitfully with an autonomous robot if the interaction is believed to be natural, without any technical knowledge of the cognitive capacities of the robot.</style></abstract></record></records></xml>