A Robotic Grasping Method Based on Deep Learning

A method is proposed to detect the optimal position of robotic grasping based on deep neural network. Compared with conventional manually-set features, the features learned by deep neural network methods are more robust and stabler, and can be applied to objects outside of the training set. In this method, the object detection algorithm based on deep learning is first used to detect the objects in the image with the classes and locations of the objects recorded. Then, the robotic grasping method based on deep learning is used to learn the grasping positions according to the object classification and detection results. Simulation experiments indicate that the proposed method can classify the objects in the images accurately, and the grasping experimental results on Universal Robot 5 verify the effectiveness of the proposed method.