Learn More
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)
The inbred mouse strains C57BL/6 and DBA/2 were subjected to the selective depletion of peripheral nervous system norepinephrine (NE) by the administration of the neurotoxin 6-hydroxydopamine (6-OH-DA). Measurement of mitogen-induced lymphocyte proliferation following peripheral axotomy with 6-OH-DA revealed that significant enhancement (up to 200% of(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)
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(More)
  • 1