  Grasping unseen objects in unconstrained, cluttered environments is an
essential skill for autonomous robotic manipulation. Despite recent progress in
full 6-DoF grasp learning, existing approaches often consist of complex
sequential pipelines that possess several potential failure points and
run-times unsuitable for closed-loop grasping. Therefore, we propose an
end-to-end network that efficiently generates a distribution of 6-DoF
parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp
representation treats 3D points of the recorded point cloud as potential grasp
contacts. By rooting the full 6-DoF grasp pose and width in the observed point
cloud, we can reduce the dimensionality of our grasp representation to 4-DoF
which greatly facilitates the learning process. Our class-agnostic approach is
trained on 17 million simulated grasps and generalizes well to real world
sensor data. In a robotic grasping study of unseen objects in structured
clutter we achieve over 90% success rate, cutting the failure rate in half
compared to a recent state-of-the-art method.
