  6D robotic grasping beyond top-down bin-picking scenarios is a challenging
task. Previous solutions based on 6D grasp synthesis with robot motion planning
usually operate in an open-loop setting, which are sensitive to grasp synthesis
errors. In this work, we propose a new method for learning closed-loop control
policies for 6D grasping. Our policy takes a segmented point cloud of an object
from an egocentric camera as input, and outputs continuous 6D control actions
of the robot gripper for grasping the object. We combine imitation learning and
reinforcement learning and introduce a goal-auxiliary actor-critic algorithm
for policy learning. We demonstrate that our learned policy can be integrated
into a tabletop 6D grasping system and a human-robot handover system to improve
the grasping performance of unseen objects. Our videos and code can be found at
https://sites.google.com/view/gaddpg .
