深度强化学习的机械臂密集场景多物体抓取方法

Robots are prone to collisions while grasping objects in cluttered scenes, relying on pushing to create space for grasping. Existing push-grasping collaborative methods demonstrate low sample efficiency and grasping success rates. To address these problems, a new deep reinforcement learning method based on DDQN (double deep Q network) is proposed to efficiently learn excellent push-grasp cooperative strategies. The system incorporates a mask function that screens effective actions, allowing the robot to focus on samples that facilitate efficient learning. Additionally, the push reward function is designed using the difference between the average relative distances of all objects in the workspace before and after pushing, which allows for a more precise assessment of the impact of candidate pushing on density. The experimental results of the method with VPG (visual pushing grasping) are analyzed to show that the proposed method accelerates the training process while improving the grasping success rate, and verify that the system can be fully adapted to real world.