Adaptive grasping strategy of robot based on Gaussian process

When robot grasps an object, the pose of the object maybe change frequently. In order to make the robot adapt to the change of the pose of the object in the process of motion, an adaptive grasping strategy of robot based on Gaussian process was proposed. The proposed method maps the observation variables to the joint angles, which makes robot learn from samples and eliminates the calibration process of robot vision system and the robot inverse kinematics computation. First, the robot was dragged to grasp object. The observation variables of object and corresponding robot joint angles were recorded. Second, Gaussian process model was trained with the recorded samples, which correlates the observation variables and joint angles. Finally, after new observation variables were acquired, joint angles for grasping operation can be obtained by the trained Gaussian process model. The experiments show that UR3 robot can successfully grasp objects after training.