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Generalizing grasps across partly similar objects
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
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2012 (English)In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2012, 3791-3797 p.Conference paper, Published paper (Refereed)
Abstract [en]

The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by learning, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.

Place, publisher, year, edition, pages
IEEE Computer Society, 2012. 3791-3797 p.
Series
IEEE International Conference on Robotics and Automation, ISSN 2152-4092
Keyword [en]
Dimensionality reduction, Grasp planning, Object shape, Proof of concept, Robot grasping, Shape analysis, Size and shape, Structure analysis, Transfer problems, Unsupervised clustering algorithm, Visual clustering
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-66389DOI: 10.1109/ICRA.2012.6224992ISI: 000309406703134Scopus ID: 2-s2.0-84864484331ISBN: 978-146731403-9 (print)OAI: oai:DiVA.org:kth-66389DiVA: diva2:483851
Conference
2012 IEEE International Conference on Robotics and Automation, RiverCentre, Saint Paul, Minnesota, USA, May 14-18,2012
Funder
ICT - The Next Generation
Note

QC 20120905

Available from: 2012-01-26 Created: 2012-01-26 Last updated: 2013-10-01Bibliographically approved

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Kragic, Danica

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Detry, RenaudEk, Carl HenrikMadry, MariannaKragic, Danica
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