  Robot learning is often simplified to planar manipulation due to its data
consumption. Then, a common approach is to use a fully-convolutional neural
network to estimate the reward of grasp primitives. In this work, we extend
this approach by parametrizing the two remaining, lateral Degrees of Freedom
(DoFs) of the primitives. We apply this principle to the task of 6 DoF bin
picking: We introduce a model-based controller to calculate angles that avoid
collisions, maximize the grasp quality while keeping the uncertainty small. As
the controller is integrated into the training, our hybrid approach is able to
learn about and exploit the model-based controller. After real-world training
of 27000 grasp attempts, the robot is able to grasp known objects with a
success rate of over 92% in dense clutter. Grasp inference takes less than
50ms. In further real-world experiments, we evaluate grasp rates in a range of
scenarios including its ability to generalize to unknown objects. We show that
the system is able to avoid collisions, enabling grasps that would not be
possible without primitive adaption.
