  Grasping in cluttered environments is a fundamental but challenging robotic
skill. It requires both reasoning about unseen object parts and potential
collisions with the manipulator. Most existing data-driven approaches avoid
this problem by limiting themselves to top-down planar grasps which is
insufficient for many real-world scenarios and greatly limits possible grasps.
We present a method that plans 6-DOF grasps for any desired object in a
cluttered scene from partial point cloud observations. Our method achieves a
grasp success of 80.3%, outperforming baseline approaches by 17.6% and clearing
9 cluttered table scenes (which contain 23 unknown objects and 51 picks in
total) on a real robotic platform. By using our learned collision checking
module, we can even reason about effective grasp sequences to retrieve objects
that are not immediately accessible. Supplementary video can be found at
https://youtu.be/w0B5S-gCsJk.
