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.

翻译：本文建立了一种新型的机器人抓取系统，用于在杂乱场景中自动拾取物体。我们设计了一种由吸盘和夹爪组成的复合机器人手爪，以实现对物体的稳定抓取。吸盘首先用于将物体从杂乱堆中抬起，随后夹爪相应地进行抓取。我们利用可操作度图（affordance map）为吸盘提供像素级的提升点候选。为获得高质量的可操作度图，系统引入了主动探索机制。我们设计了一种有效的度量标准来计算当前可操作度图的奖励值，并采用深度Q网络（DQN）引导机器人手爪主动探索环境，直至生成的可操作度图适合抓取。实验结果表明，所提出的机器人抓取系统能够显著提高杂乱场景下机器人抓取的成功率。