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.
JF - 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI) PB - IEEE CY - La Jolla, CA SN - 978-1-60558-404-1 ER - TY - CONF T1 - Building a more effective teaching robot using apprenticeship learning T2 - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 Y1 - 2008 A1 - Ruvolo, P. A1 - Whitehill, J. A1 - Virnes, M. A1 - Movellan, J. KW - apprenticeship learning KW - automated helicopter flight KW - Automatic control KW - Data mining KW - Delay KW - education KW - Educational robots KW - expert teaching KW - Helicopters KW - Human-robot interaction KW - humanoid robots KW - Humans Learning systems KW - mechanical control KW - robot teaching KW - Robotics and Automation KW - RUBI social robot KW - time 18 month to 24 month KW - timing AB -What 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.
JF - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 PB - IEE CY - Monterey, CA SN - 978-1-4244-2661-4 ER -