A Novel Robotic Grasping Method for Moving Objects Based on Multi-Agent Deep Reinforcement Learning

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, MATD3H 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 realworld 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.