  This paper presents a novel approach for the automatic offline grasp pose
synthesis on known rigid objects for parallel jaw grippers. We use several
criteria such as gripper stroke, surface friction, and a collision check to
determine suitable 6D grasp poses on an object. In contrast to most available
approaches, we neither aim for the best grasp pose nor for as many grasp poses
as possible, but for a highly diverse set of grasps distributed all along the
object. In order to accomplish this objective, we employ a clustering algorithm
to the sampled set of grasps. This allows to simultaneously reduce the set of
grasp pose candidates and maintain a high variance in terms of position and
orientation between the individual grasps. We demonstrate that the grasps
generated by our method can be successfully used in real-world robotic grasping
applications.
