[Robot Perception and Learning] Lab Meeting Fri, 2 March 2007 : Robotic Grasping of Novel Objects

Ashutosh Saxena, Justin Driemeyer, Justin Kearns, Andrew Y.Ng
Computer Science Department
Stanford University, Stanford, CA 94305

[link]


Abstract:

We consider the problem of grasping novel objects, specifically ones that are being seen for the first time through vision. We present a learning algorithm that
neither requires, nor tries to build, a 3-d model of the object. Instead it predicts,
directly as a function of the images, a point at which to grasp the object. Our algorithm is trained via supervised learning, using synthetic images for the training set. We demonstrate on a robotic manipulation platform that this approach successfully grasps a wide variety of objects, such as wine glasses, duct tape, markers, atranslucentbox, jugs, knife-cutters, cellphones, keys, screwdrivers, staplers, toothbrushes, a thick coil of wire, a strangely shaped power horn, and others, none of which were seen in the training set.