  Extracting a known target object from a pile of other objects in a cluttered
environment is a challenging robotic manipulation task encountered in many
robotic applications. In such conditions, the target object touches or is
covered by adjacent obstacle objects, thus rendering traditional grasping
techniques ineffective. In this paper, we propose a pushing policy aiming at
singulating the target object from its surrounding clutter, by means of lateral
pushing movements of both the neighboring objects and the target object until
sufficient 'grasping room' has been achieved. To achieve the above goal we
employ reinforcement learning and particularly Deep Q-learning (DQN) to learn
optimal push policies by trial and error. A novel Split DQN is proposed to
improve the learning rate and increase the modularity of the algorithm.
Experiments show that although learning is performed in a simulated environment
the transfer of learned policies to a real environment is effective thanks to
robust feature selection. Finally, we demonstrate that the modularity of the
algorithm allows the addition of extra primitives without retraining the model
from scratch.
