@article{13092660v1,
  title = {Data-Driven Grasp Synthesis - A Survey},
  journal = {IEEE Transactions on Robotics},
  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 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.},
  volume = {30},
  pages = {289 - 309},
  publisher = {IEEE},
  month = apr,
  year = {2014},
  slug = {13092660v1},
  author = {Bohg, J. and Morales, A. and Asfour, T. and Kragic, D.},
  url = {http://www.robotic-grasping.com},
  month_numeric = {4}
}