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.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

A 3D descriptor to detect task-oriented grasping points in clothing

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.Peer ReviewedPostprint (author's final draft