  Many possible fields of application of robots in real world settings hinge on
the ability of robots to grasp objects. As a result, robot grasping has been an
active field of research for many years. With our publication we contribute to
the endeavor of enabling robots to grasp, with a particular focus on bin
picking applications. Bin picking is especially challenging due to the often
cluttered and unstructured arrangement of objects and the often limited
graspability of objects by simple top down grasps. To tackle these challenges,
we propose a fully self-supervised reinforcement learning approach based on a
hybrid discrete-continuous adaptation of soft actor-critic (SAC). We employ
parametrized motion primitives for pushing and grasping movements in order to
enable a flexibly adaptable behavior to the difficult setups we consider.
Furthermore, we use data augmentation to increase sample efficiency. We
demonnstrate our proposed method on challenging picking scenarios in which
planar grasp learning or action discretization methods would face a lot of
difficulties
