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.
10 aActuators 10 abarebones communicative robot 10 aCommunication system control 10 aDelay 10 aDetectors 10 aHuman robot interaction 10 ahuman-model updating capability 10 ahumanoid robots 10 aHydrogen 10 aInfomax control 10 aman-machine systems 10 aPediatrics 10 apolicy improvement 10 aRobot control 10 aRobot sensing systems 10 aScheduling 10 asocial contingency 1 aTanaka, F. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/barebones-communicative-robot-based-social-contingency-and-infomax-control 02582nas a2200373 4500008004100000020002200041245007500063210006900138260003100207520141200238653002801650653003201678653002201710653001601732653001001748653001401758653002301772653002001795653001601815653002801831653002001859653002801879653002301907653001901930653002801949653002201977653003001999653001102029100001502040700001802055700001502073700001702088856010302105 2008 eng d a978-1-4244-2661-4 00 aBuilding a more effective teaching robot using apprenticeship learning 0 aBuilding a more effective teaching robot using apprenticeship le aMonterey, CA bIEE c08/2008 3 aWhat 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.
10 aapprenticeship learning 10 aautomated helicopter flight 10 aAutomatic control 10 aData mining 10 aDelay 10 aeducation 10 aEducational robots 10 aexpert teaching 10 aHelicopters 10 aHuman-robot interaction 10 ahumanoid robots 10 aHumans Learning systems 10 amechanical control 10 arobot teaching 10 aRobotics and Automation 10 aRUBI social robot 10 atime 18 month to 24 month 10 atiming 1 aRuvolo, P. 1 aWhitehill, J. 1 aVirnes, M. 1 aMovellan, J. uhttps://rubi.ucsd.edu/content/building-more-effective-teaching-robot-using-apprenticeship-learning