An Autonomous Grasping Strategy Based on Two-Stage Progressive Network Guidedby Prior Knowledge

To grasp stacked objects with unknown and irregular shapes, an autonomous grasping method based on two-stage progressive network (TSPN) is proposed. Firstly, the global grasping distribution is acquired based on end-to-end strategy, and then the optimal grasping configuration is determined based on the sampling-evaluation strategy. By combining the above two strategies, the structure of TSPN is simplified, the number of samples to be evaluated is reduced significantly, and the generalization ability is guaranteed while the grasping efficiency is improved. In order to speed up the learning process of the grasping model, a self-supervised learning strategy guided by prior knowledge is introduced, and 220 irregular objects are used for grasping learning. Experiments are carried out in simulation and real environment respectively. The results show that the proposed grasping model is suitable for the grasping scenes with multiple objects, stacked objects, unknown irregular objects, and the objects with random position and pose. The grasping accuracy and detection speed are significantly improved compared with other reference methods. The whole learning process lasted for 10 days, and the results show that the learning strategy guided by prior knowledge can significantly speed up the learning process.