A novel robotic grasping method for moving objects based on multi-agent deep reinforcement learning

Abstract

To grasp the randomly moving objects in unstructured environment, a novel robotic grasping method based on multi-agent TD3 with high-quality memory (MA-TD3H) is proposed. During the grasping process, the MA-TD3H algorithm obtains the object's motion state from the vision detection module and outputs the velocity of the gripper. The quality of the sampled memory plays a crucial role in reinforcement learning models. In MA-TD3H, transitions are saved in the memory buffer and high-quality memory (H-memory) buffer respectively. When updating the actor network, transitions are adaptively sampled from the two buffers by a set ratio according to the current grasping success rate of the algorithm. Also, the multi-agent mechanism enables the MA-TD3H algorithm to control multiple agents for simultaneous training and experience sharing. In the simulation, MA-TD3H improves the success rate of grasping the moving object by around 25 percent, compared with TD3, DDPG and SAC. While in most cases, MA-TD3H spends 80 percent of the time of the other algorithms. In real-world experiments on grasping objects in different shapes and trajectories, the average grasping prediction success rate (GPSR) and grasping reaching success rate (GRSR) of MA-TD3H are above 90 percent and 80 percent respectively, and the average GRSR is improved by 20–30 percent compared with the other algorithms. In summary, simulated and real-world experiments validate that the MA-TD3H algorithm outperforms the other algorithms in robotic grasping for moving objects.