  In this paper, a novel robotic grasping system is established to
automatically pick up objects in cluttered scenes. A composite robotic hand
composed of a suction cup and a gripper is designed for grasping the object
stably. The suction cup is used for lifting the object from the clutter first
and the gripper for grasping the object accordingly. We utilize the affordance
map to provide pixel-wise lifting point candidates for the suction cup. To
obtain a good affordance map, the active exploration mechanism is introduced to
the system. An effective metric is designed to calculate the reward for the
current affordance map, and a deep Q-Network (DQN) is employed to guide the
robotic hand to actively explore the environment until the generated affordance
map is suitable for grasping. Experimental results have demonstrated that the
proposed robotic grasping system is able to greatly increase the success rate
of the robotic grasping in cluttered scenes.
