  This paper introduces a novel approach for the grasping and precise placement
of various known rigid objects using multiple grippers within highly cluttered
scenes. Using a single depth image of the scene, our method estimates multiple
6D object poses together with an object class, a pose distance for object pose
estimation, and a pose distance from a target pose for object placement for
each automatically obtained grasp pose with a single forward pass of a neural
network. By incorporating model knowledge into the system, our approach has
higher success rates for grasping than state-of-the-art model-free approaches.
Furthermore, our method chooses grasps that result in significantly more
precise object placements than prior model-based work.
