Data-Driven Grasp Synthesis—A Survey
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Data-Driven Grasp Synthesis—A SurveyDate
2014-04xmlui.dri2xhtml.METS-1.0.item-edition
PostprintPublisher
Institute of Electrical and Electronics Engineers (IEEE)ISSN
1552-3098Bibliographic citation
BOHG, Jeannette, et al. Data-driven grasp synthesis—a survey. Robotics, IEEE Transactions on, 2014, vol. 30, no 2, p. 289-309.Type
info:eu-repo/semantics/articlePublisher version
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6672028&tag=1Subject
Abstract
We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, ... [+]
We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations. [-]
Is part of
IEEE Transactions on Robotics, 2014, vol. 30, no 2Rights
info:eu-repo/semantics/openAccess
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- ICC_Articles [430]
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