Before revision:  The supervision on grasping goals can be obtained from the expert planner for known objects or from hindsight goals for unknown objects. Overall, our learned closed-loop policy achieves over 90\% success rates on grasping various ShapeNet objects and YCB objects in simulation. The policy also transfers well to the real world for grasping unseen objects in both a tabletop setting and a human-robot handover setting in our experiments.
After revision:  We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover setting in our experiments.