  Robotic grasp detection is a fundamental capability for intelligent
manipulation in unstructured environments. Previous work mainly employed visual
and tactile fusion to achieve stable grasp, while, the whole process depending
heavily on regrasping, which wastes much time to regulate and evaluate. We
propose a novel way to improve robotic grasping: by using learned tactile
knowledge, a robot can achieve a stable grasp from an image. First, we
construct a prior tactile knowledge learning framework with novel grasp quality
metric which is determined by measuring its resistance to external
perturbations. Second, we propose a multi-phases Bayesian Grasp architecture to
generate stable grasp configurations through a single RGB image based on prior
tactile knowledge. Results show that this framework can classify the outcome of
grasps with an average accuracy of 86% on known objects and 79% on novel
objects. The prior tactile knowledge improves the successful rate of 55% over
traditional vision-based strategies.
