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We consider the problem of grasping novel objects, specifically ones 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. Further, even if given a model, one still has to decide where to grasp the object. We present a learning algorithm that neither(More)
— 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 which predicts, as a function of the images, the position at which to grasp the object. This is done without building or requiring a 3-d model of the object. Our algorithm is trained via supervised(More)
— We propose a learning algorithm for estimating the 3-D orientation of objects. Orientation learning is a difficult problem because the space of orientations is non-Euclidean, and in some cases (such as quaternions) the representation is ambiguous, in that multiple representations exist for the same physical orientation. Learning is further complicated by(More)
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