A Robot Self-learning Grasping Control Method Based on Gaussian Process and Bayesian Algorithm

A robot self-learning grasping control method combining Gaussian process and Bayesian algorithm was proposed. The grasping gesture and parameters of the robot end-effector were adjusted according to the position and pose changes of target location to realize accurate grasping of the target. Firstly, a robot self-adaptive grasping method based on Gaussian process was proposed for grasping training in order to realize modeling and matching of position and pose information of target object and robot joint variables. The trained Gaussian process model is combined with Bayesian algorithm. The model was taken as priori knowledge and the semi-supervised self-learning was implemented in new grasping region so that posterior Gaussian process model was generated. This method omits the complex visual calibration process and inverse kinematics solves only with a small group of samples. Besides, when the environment of grasping changes, the previous learning experience can be used to perform self-learning, and adapt to the grasping task in the new environment, which reduces the workload of operators. The effectiveness of the robot self-learning grasping control method based on Gaussian process and Bayesian algorithm was verified through simulation and grasping experiment of UR3.