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.

本文提出了一种新颖的机器人抓取系统，由气泡吸盘和机械手抓手组成。利用可供性地图提供像素级的气泡吸盘升力点候选物，并引入主动探索机制，设计了一种有效的度量来计算当前可供性地图的奖励，并使用深度 Q 网络（DQN）指导机器人手探索环境，实验结果表明，所提出的机器人抓取系统能够大大提高在混乱场景中的机器人抓取成功率。