Header logo is am

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.

Author(s): Bohg, J. and Morales, A. and Asfour, T. and Kragic, D.
Journal: IEEE Transactions on Robotics
Volume: 30
Pages: 289 - 309
Year: 2014
Month: April
Publisher: IEEE

Department(s): Autonomous Motion
Research Project(s): Learning to Grasp from Big Data
Bibtex Type: Article (article)

DOI: http://dx.doi.org/10.1109/TRO.2013.2289018
State: Published
URL: http://www.robotic-grasping.com

Links: PDF


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