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Robotic grasp detection is a fundamental capability for intelligentmanipulation in unstructured environments. Previous work mainly employed visualand tactile fusion to achieve stable grasp, while, the whole process dependingheavily on regrasping, which wastes much time to regulate and evaluate. Wepropose a novel way to improve robotic grasping: by using learned tactileknowledge, a robot can achieve a stable grasp from an image. First, weconstruct a prior tactile knowledge learning framework with novel grasp qualitymetric which is determined by measuring its resistance to externalperturbations. Second, we propose a multi-phases Bayesian Grasp architecture togenerate stable grasp configurations through a single RGB image based on priortactile knowledge. Results show that this framework can classify the outcome ofgrasps with an average accuracy of 86% on known objects and 79% on novelobjects. The prior tactile knowledge improves the successful rate of 55% overtraditional vision-based strategies.

Bayesian Grasp: Robotic visual stable grasp based on prior tactile knowledge