  In this paper, we introduce a novel learning-based approach for grasping
known rigid objects in highly cluttered scenes and precisely placing them based
on depth images. Our Placement Quality Network (PQ-Net) estimates the object
pose and the quality for each automatically generated grasp pose for multiple
objects simultaneously at 92 fps in a single forward pass of a neural network.
All grasping and placement trials are executed in a physics simulation and the
gained experience is transferred to the real world using domain randomization.
We demonstrate that our policy successfully transfers to the real world. PQ-Net
outperforms other model-free approaches in terms of grasping success rate and
automatically scales to new objects of arbitrary symmetry without any human
intervention.
