NDLI: Learning probabilistic discriminative models of grasp affordances under limited supervision

Content Provider	IEEE Xplore Digital Library
Author	Erkan, A.N. Kroemer, O. Detry, R. Altun, Y. Piater, J. Peters, J.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering and Computer Science Montefiore Institute, Université de Liège 4000 Sart Tilman Belgium (Detry, R.; Piater, J.) \|\| Max Plank Institute for Biological Cybernetics, Spemannstraße 38, Tuebingen Germany (Erkan, A.N.; Kroemer, O.; Altun, Y.; Peters, J.)
Abstract	This paper addresses the problem of learning and efficiently representing discriminative probabilistic models of object-specific grasp affordances particularly when the number of labeled grasps is extremely limited. The proposed method does not require an explicit 3D model but rather learns an implicit manifold on which it defines a probability distribution over grasp affordances. We obtain hypothetical grasp configurations from visual descriptors that are associated with the contours of an object. While these hypothetical configurations are abundant, labeled configurations are very scarce as these are acquired via time-costly experiments carried out by the robot. Kernel logistic regression (KLR) via joint kernel maps is trained to map the hypothesis space of grasps into continuous class-conditional probability values indicating their achievability. We propose a soft-supervised extension of KLR and a framework to combine the merits of semi-supervised and active learning approaches to tackle the scarcity of labeled grasps. Experimental evaluation shows that combining active and semi-supervised learning is favorable in the existence of an oracle. Furthermore, semi-supervised learning outperforms supervised learning, particularly when the labeled data is very limited.
Starting Page	1586
Ending Page	1591
File Size	1700094
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5650088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Kernel Training Joints Mathematical model Three dimensional displays Probabilistic logic
Content Type	Text
Resource Type	Article

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