This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Saxena, A. Articles by Ng, A. Y. Search for Related Content Social Bookmarking                         What's this?

Robotic Grasping of Novel Objects using Vision

Computer Science Department, Stanford University, Stanford, CA 94305, USA, asaxena{at}cs.stanford.edu

Justin Driemeyer

Computer Science Department, Stanford University, Stanford, CA 94305, USA, jdriemeyer{at}cs.stanford.edu

Andrew Y. Ng

Computer Science Department, Stanford University, Stanford, CA 94305, USA, ang{at}cs.stanford.edu

We consider the problem of grasping novel objects, specifically objects that are being seen for the first time through vision. Grasping a previously unknown object, one for which a 3-d model is not available, is a challenging problem. Furthermore, even if given a model, one still has to decide where to grasp the object. We present a learning algorithm that neither requires nor tries to build a 3-d model of the object. Given two (or more) images of an object, our algorithm attempts to identify a few points in each image corresponding to good locations at which to grasp the object. This sparse set of points is then triangulated to obtain a 3-d location at which to attempt a grasp. This is in contrast to standard dense stereo, which tries to triangulate every single point in an image (and often fails to return a good 3-d model). Our algorithm for identifying grasp locations from an image is trained by means of supervised learning, using synthetic images for the training set. We demonstrate this approach on two robotic manipulation platforms. Our algorithm successfully grasps a wide variety of objects, such as plates, tape rolls, jugs, cellphones, keys, screwdrivers, staplers, a thick coil of wire, a strangely shaped power horn and others, none of which were seen in the training set. We also apply our method to the task of unloading items from dishwashers.

Key Words: grasping • vision of grasping • learning and adaptive systems • personal robots • robotics • perception

The International Journal of Robotics Research, Vol. 27, No. 2, 157-173 (2008)
DOI: 10.1177/0278364907087172


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. T. Ciocarlie and P. K. Allen Hand Posture Subspaces for Dexterous Robotic Grasping The International Journal of Robotics Research, July 1, 2009; 28(7): 851 - 867. [Abstract] [PDF]