<?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%">Markelius, A.</style></author><author><style face="normal" font="default" size="100%">Sjöberg, S.</style></author><author><style face="normal" font="default" size="100%">Lemhaouri, Z.</style></author><author><style face="normal" font="default" size="100%">Cohen, L.</style></author><author><style face="normal" font="default" size="100%">Lowe, R.</style></author><author><style face="normal" font="default" size="100%">Cañamero, L.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Abdulaziz Al Ali</style></author><author><style face="normal" font="default" size="100%">Nader Meskin</style></author><author><style face="normal" font="default" size="100%">Wanyue Jiang</style></author><author><style face="normal" font="default" size="100%">Shuzhi Sam Ge</style></author><author><style face="normal" font="default" size="100%">John-John Cabibihan</style></author><author><style face="normal" font="default" size="100%">Silvia Rossi</style></author><author><style face="normal" font="default" size="100%">Hongsheng He</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Human-Robot Mutual Learning System with Affect-Grounded Language Acquisition and Differential Outcomes Training</style></title><secondary-title><style face="normal" font="default" size="100%">Social Robotics. 15th International Conference, ICSR 2023, Proceedings Part II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-981-99-8718-4</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Doha, Qatar, December 3–7, 2023</style></pub-location><volume><style face="normal" font="default" size="100%">LNAI 14454</style></volume><pages><style face="normal" font="default" size="100%">108–122</style></pages><isbn><style face="normal" font="default" size="100%">978-981-99-8717-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></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>