Data-Driven Grasp Synthesis - A Survey

TL;DR

This survey comprehensively reviews data-driven methods for robot grasp synthesis, categorizing approaches based on object familiarity and highlighting common representations, perceptual processes, and open challenges.

Contribution

It provides a structured overview of data-driven grasp synthesis techniques, comparing them with classical analytic methods and identifying key open problems.

Findings

Categorizes grasp synthesis methods into known, familiar, and unknown object approaches.

Highlights the importance of object representations and perceptual processes.

Discusses open problems and future directions in robot grasping.

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.