Abstract: Considering the problem that the robot needs to estimate the grasp quality in real time to dynamically adjust the grasp configuration in grasping process, a stable robotic grasp method based on prior tactile knowledge learning is proposed. Firstly, a tactile information based method is put forward for evaluating grasp quality according to the capacity of resisting the external perturbations in grasping process. Based on it, a visual-tactile joint dampest is built, and the prior tactile knowledge is learned. Secondly, an architecture is proposed to generate stable grasp configurations by fusing the visual image and the prior tactile knowledge. Finally, an experimental verification with 10 kinds of objects is carried out on the proposed robotic grasp system. The results show that the grasp stability is improved by 55% with the proposed method over traditional vision-based methods, and the success rates of stable grasp are 86% and 79% respectively on known and unknown objects, demonstrating good generalization performance of the proposed method.