Data-Driven Grasp Synthesis - A Survey
This is an incremental survey that organizes existing research for researchers in robotics and AI, without introducing new methods.
The paper surveys data-driven grasp synthesis methods, categorizing them by object familiarity and discussing common representations and perceptual processes, while also highlighting open problems and comparing with classical analytic approaches.
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.