Taking advantage of its high?speed and high?precision characteristics, cooperative robots can improve production efficiency by imitating human creative and complex actions. At present, the simulation of human action mainly comes from the long?term debugging of the deployment personnel, which is lack of general solutions and can′t be deployed quickly. Based on this, an anchor?free RepVGG network?based Siamese network collaborative robot target tracking algorithm was proposed. The algorithm consists of a siamese network module, a classification regression module and a robot execution module. The siamese network module used the improved RepVGG network instead of ResNet as the backbone network to extract image features, which can improve the running speed of the whole network without losing accuracy，reduce the hardware requirements and is more friendly to special deep learning chips; the classification and regression module introduced the centrality branch to improve the prediction accuracy of the center point of the tracking frame; the robot execution module uses scale penalty and aspect ratio penalty to smooth the tracking boxes and ensure the smooth operation of the collaborative robots. Experimental results show that the average rate is 14 FPS higher than that before replacing the backbone network, which realizes real?time object tracking.