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Proceedings of the Post-Graduate Conference on Robotics and Development of Cognition, 10-12 September 2012, Lausanne, Switzerland
Get PDFThe aim of the Postgraduate Conference on Robotics and Development of Cognition (RobotDoC-PhD) is to bring together young scientists working on developmental cognitive robotics and its core disciplines. The conference aims to provide both feedback and greater visibility to their research as lively and stimulating discussion can be held amongst participating PhD students and senior researchers. The conference is open to all PhD students and post-doctoral researchers in the field. RobotDoC-PhD conference is an initiative as a part of Marie-Curie Actions ITN RobotDoC and will be organized as a satellite event of the 22nd International Conference on Artificial Neural Networks ICANN 2012
Reachable by walking: inappropriate integration of near and far space may lead to distance errors
Get PDFOur experimental results show that infants while learning to walk intend to reach for unreachable objects. These distance errors may result from inappropriate integration of reaching and locomotor actions, attention control and near/far visual space. Infants during their first months are fairly immobile, their attention and actions are constrained to near (reachable) space. Walking, in contrast, lures attention to distal displays and provides the information to disambiguate far space. In this paper, we make use of a reward-mediated learning to mimic the development of absolute distance perception. The results obtained with the NAO robot support further our hypothesis that the representation of near space changes after the onset of walking, which may cause the occurrence of distance errors
Die Entwicklung der Nutzung elektronischer Zeitschriften an der Universität Bielefeld: eine statistische Auswertung
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Whom Will an Intrinsically Motivated Robot Learner Choose to Imitate from?
Get PDFThis paper studies an interactive learning system that couples internally guided learning and social interaction in the case it can interact with several teachers. Socially Guided Intrinsic Motivation with Interactive learning at the Meta level (SGIMIM) is an algorithm for robot learning of motor skills in highdimensional, continuous and non-preset environments, with two levels of active learning: SGIM-IM actively decides at a metalevel when and to whom to ask for help; and an active choice of goals in autonomous exploration. We illustrate through an air hockey game that SGIM-IM efficiently chooses the best strategy
:Fortbildungstermin: Discovery Services: Entwicklung und erste Ergebnisse
No full texthttps://www.ub.uni-bielefeld.de/library/databases/eds.ht
