Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 30, Issue 2, Pages 289-309Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2013.2289018
Keywords
Grasp planning; grasp synthesis; object grasping and manipulation; object recognition and classification; visual perception; visual representations
Categories
Funding
- FLEXBOT [FP7-ERC-279933]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1017134, 0917318] Funding Source: National Science Foundation
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available