Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Grasp Synthesis - A Survey   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.

27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin