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  Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision

Erkan, A., Kroemer, O., Detry, R., Altun, Y., Piater, J., & Peters, J. (2010). Learning Probabilistic Discriminative Models of Grasp Affordances under Limited Supervision. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010) (pp. 1586-1591). Piscataway, NJ, USA: IEEE.

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 Creators:
Erkan, AN1, 2, Author              
Kroemer, O1, 2, Author              
Detry, R, Author
Altun, Y1, 2, Author              
Piater, J, Author              
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2010-10
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/IROS.2010.5650088
BibTex Citekey: 6618
 Degree: -

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Title: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Place of Event: Taipei, Taiwan
Start-/End Date: 2010-10-18 - 2010-10-22

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Title: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1586 - 1591 Identifier: ISBN: 978-1-424-46675-7