Data-Driven Grasp Synthesis-A Survey
2014 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, Vol. 30, no 2, 289-309 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2014. Vol. 30, no 2, 289-309 p.
Grasp planning, grasp synthesis, object grasping and manipulation, object recognition and classification, visual perception, visual representations
IdentifiersURN: urn:nbn:se:kth:diva-145598DOI: 10.1109/TRO.2013.2289018ISI: 000334596700001ScopusID: 2-s2.0-84898489752OAI: oai:DiVA.org:kth-145598DiVA: diva2:723594
FunderEU, FP7, Seventh Framework Programme, FP7-ERC-279933
QC 201406112014-06-112014-05-232014-06-11Bibliographically approved