@conference {61, title = {A barebones communicative robot based on social contingency and Infomax Control}, booktitle = {The 17th IEEE International Symposium on Robot and Human Interactive Communication, 2008. RO-MAN 2008}, year = {2008}, month = {08/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Munich}, abstract = {

In this paper, we present a barebones robot which is capable of interacting with humans based on social contingency. It expands the previous work of a contingency detector into having both human-model updating (developmental capability) and policy improvement (learning capability) based on the framework of Infomax control. The proposed new controller interacts with humans in both active and responsive ways handling the turn-taking between them.

}, keywords = {Actuators, barebones communicative robot, Communication system control, Delay, Detectors, Human robot interaction, human-model updating capability, humanoid robots, Hydrogen, Infomax control, man-machine systems, Pediatrics, policy improvement, Robot control, Robot sensing systems, Scheduling, social contingency}, isbn = {978-1-4244-2212-8}, author = {Tanaka, F. and Movellan, J.} } @conference {59, title = {Building a more effective teaching robot using apprenticeship learning}, booktitle = {7th IEEE International Conference on Development and Learning, 2008. ICDL 2008}, year = {2008}, month = {08/2008}, publisher = {IEE}, organization = {IEE}, address = {Monterey, CA}, abstract = {

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.

}, keywords = {apprenticeship learning, automated helicopter flight, Automatic control, Data mining, Delay, education, Educational robots, expert teaching, Helicopters, Human-robot interaction, humanoid robots, Humans Learning systems, mechanical control, robot teaching, Robotics and Automation, RUBI social robot, time 18 month to 24 month, timing}, isbn = {978-1-4244-2661-4}, author = {Ruvolo, P. and Whitehill, J. and Virnes, M. and Movellan, J.} }