Data-Driven Grasp Synthesis - a Survey Jeannette Bohg, Member, IEEE, Antonio Morales, Member, IEEE, Tamim Asfour, Member, IEEE, Danica Kragic Member, IEEE
TRANSACTIONS ON ROBOTICS 1 Data-Driven Grasp Synthesis - A Survey Jeannette Bohg, Member, IEEE, Antonio Morales, Member, IEEE, Tamim Asfour, Member, IEEE, Danica Kragic Member, IEEE specific metric. This process is usually based on some existing Abstract—We review the work on data-driven grasp synthesis grasp experience that can be a heuristic or is generated in and the methodologies for sampling and ranking candidate simulation or on a real robot. Kamon et al. [5] refer to this grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown as the comparative and Shimoga [2] as the knowledge-based objects. This structure allows us to identify common object rep- approach. Here, a grasp is commonly parameterized by [6, 7]: resentations and perceptual processes that facilitate the employed • the grasping point on the object with which the tool center data-driven grasp synthesis technique. In the case of known point (TCP) should be aligned, objects, we concentrate on the approaches that are based on • the approach vector which describes the 3D angle that object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching the robot hand approaches the grasping point with, to a set of previously encountered objects. Finally, for the • the wrist orientation of the robotic hand and approaches dealing with unknown objects, the core part is the • an initial finger configuration extraction of specific features that are indicative of good grasps. Data-driven approaches differ in how the set of grasp candi- Our survey provides an overview of the different methodologies dates is sampled, how the grasp quality is estimated and how and discusses open problems in the area of robot grasping.
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