Robotic grasp detection using effective graspable feature selection and precise classification

Abstract

It is necessary to implement real-time grasp detection in robotic grasping tasks. To this end, in this paper we propose a method for effective graspable feature selection and precise classification. In a robotic grasping scene, our method can effectively select graspable rectangles and further extract useful features from them to generate a feature set. A convolutional neural network (CNN) is then developed to score and classify the elements in the feature set. Finally, we compute the desired robotic grasp pose based on the graspable feature that gets the highest score. In the test phase the proposed CNN network achieves an accuracy of 96.5% on the Cornell Grasping Dataset. In real-world grasping experiments 105 frames per second (fps) for the object's grasp detection and a grasp success rate of 89.9% have been achieved with our method.