  Recently, a number of grasp detection methods have been proposed that can be
used to localize robotic grasp configurations directly from sensor data without
estimating object pose. The underlying idea is to treat grasp perception
analogously to object detection in computer vision. These methods take as input
a noisy and partially occluded RGBD image or point cloud and produce as output
pose estimates of viable grasps, without assuming a known CAD model of the
object. Although these methods generalize grasp knowledge to new objects well,
they have not yet been demonstrated to be reliable enough for wide use. Many
grasp detection methods achieve grasp success rates (grasp successes as a
fraction of the total number of grasp attempts) between 75% and 95% for novel
objects presented in isolation or in light clutter. Not only are these success
rates too low for practical grasping applications, but the light clutter
scenarios that are evaluated often do not reflect the realities of real world
grasping. This paper proposes a number of innovations that together result in a
significant improvement in grasp detection performance. The specific
improvement in performance due to each of our contributions is quantitatively
measured either in simulation or on robotic hardware. Ultimately, we report a
series of robotic experiments that average a 93% end-to-end grasp success rate
for novel objects presented in dense clutter.
