We report results of a study in which a low cost sociable robot was immersed at an Early Childhood Education Center for a period of 2 weeks. The study was designed to investigate whether the robot, which operated fully autonomously during the intervention period, could improve target vocabulary skills of 18-24 month of age toddlers. The results showed a 27% improvement in knowledge of the target words taught by the robot when compared to a matched set of control words. The results suggest that sociable robots may be an effective and low cost technology to enrich Early Childhood Education environments.
%B 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI) %I IEEE %C La Jolla, CA %8 03/2009 %@ 978-1-60558-404-1 %G eng %M 12908586 %0 Conference Paper %B IEEE International Conference on Robotics and Automation, 2008. ICRA 2008 %D 2008 %T Auditory mood detection for social and educational robots %A Ruvolo, P. %A Fasel, I. %A Movellan, J. %K auditory mood detection %K Computer vision %K educational robot %K Educational robots %K Emotion recognition %K emotional speech database %K face detection %K hearing %K interactive robotic application %K learning (artificial intelligence) %K Machine Learning %K Mood Prototypes %K object recognition %K Robotics and Automation Robots %K social mood %K social robot %K Speech %K USA Councils %XSocial robots face the fundamental challenge of detecting and adapting their behavior to the current social mood. For example, robots that assist teachers in early education must choose different behaviors depending on whether the children are crying, laughing, sleeping, or singing songs. Interactive robotic applications require perceptual algorithms that both run in real time and are adaptable to the challenging conditions of daily life. This paper explores a novel approach to auditory mood detection which was born out of our experience immersing social robots in classroom environments. We propose a new set of low-level spectral contrast features that extends a class of features which have proven very successful for object recognition in the modern computer vision literature. Features are selected and combined using machine learning approaches so as to make decisions about the ongoing auditory mood. We demonstrate excellent performance on two standard emotional speech databases (the Berlin Emotional Speech [W. Burkhardt et al., 2005], and the ORATOR dataset [H. Quast, 2001]). In addition we establish strong baseline performance for mood detection on a database collected from a social robot immersed in a classroom of 18-24 months old children [J. Movellan er al., 2007]. This approach operates in real time at little computational cost. It has the potential to greatly enhance the effectiveness of social robots in daily life environments.
%B IEEE International Conference on Robotics and Automation, 2008. ICRA 2008 %I IEEE %C Pasadena, CA %8 05/2008 %@ 978-1-4244-1646-2 %G eng %M 10014826 %0 Conference Paper %B 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 %D 2008 %T Automatic cry detection in early childhood education settings %A Ruvolo, P. %A Movellan, J. %K Acoustic noise %K auditory moods %K automatic cry detection %K behavioural sciences computing %K Deafness %K early childhood education settings %K education %K Educational robots %K Emotion recognition %K human coders %K Humans %K learning (artificial intelligence) %K Machine Learning %K Mood %K preschool classrooms %K Prototypes %K Robustness %K Working environment noise %XWe present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].
%B 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 %I IEEE %C Monterey, CA %8 08/2008 %@ 978-1-4244-2661-4 %G eng %M 10367600 %0 Conference Paper %B 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 %D 2008 %T Building a more effective teaching robot using apprenticeship learning %A Ruvolo, P. %A Whitehill, J. %A Virnes, M. %A Movellan, J. %K apprenticeship learning %K automated helicopter flight %K Automatic control %K Data mining %K Delay %K education %K Educational robots %K expert teaching %K Helicopters %K Human-robot interaction %K humanoid robots %K Humans Learning systems %K mechanical control %K robot teaching %K Robotics and Automation %K RUBI social robot %K time 18 month to 24 month %K timing %XWhat defines good teaching? While attributes such as timing, responsiveness to social cues, and pacing of material clearly play a role, it is difficult to create a comprehensive specification of what it means to be a good teacher. On the other hand, it is relatively easy to obtain examples of expert teaching behavior by observing a real teacher. With this inspiration as our guide, we investigated apprenticeship learning methods [1] that use data recorded from expert teachers as a means of improving the teaching abilities of RUBI, a social robot immersed in a classroom of 18-24 month old children. While this approach has achieved considerable success in mechanical control, such as automated helicopter flight [2], until now there has been little work on applying it to the field of social robotics. This paper explores two particular approaches to apprenticeship learning, and analyzes the models of teaching that each approach learns from the data of the human teacher. Empirical results indicate that the apprenticeship learning paradigm, though still nascent in its use in the social robotics field, holds promise, and that our proposed methods can already extract meaningful teaching models from demonstrations of a human expert.
%B 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 %I IEE %C Monterey, CA %8 08/2008 %@ 978-1-4244-2661-4 %G eng %M 10367601