  Given the task of learning robotic grasping solely based on a depth camera
input and gripper force feedback, we derive a learning algorithm from an
applied point of view to significantly reduce the amount of required training
data. Major improvements in time and data efficiency are achieved by: Firstly,
we exploit the geometric consistency between the undistorted depth images and
the task space. Using a relative small, fully-convolutional neural network, we
predict grasp and gripper parameters with great advantages in training as well
as inference performance. Secondly, motivated by the small random grasp success
rate of around 3%, the grasp space was explored in a systematic manner. The
final system was learned with 23000 grasp attempts in around 60h, improving
current solutions by an order of magnitude. For typical bin picking scenarios,
we measured a grasp success rate of 96.6%. Further experiments showed that the
system is able to generalize and transfer knowledge to novel objects and
environments.
