A novel robotic grasp detection method based on region proposal networks

Grasp detection based on deep learning is an important method for robots to accurately perceive unstructured environments. However, the deep learning method widely used in general object detection is not suitable for robotic grasp detection. Multi-stage network is often designed to meet the requirements of grasp posture, but they increase computation complexity. This paper proposes a single-stage robotic grasp detection method by using region proposal networks. The proposed method generates multiple oriented reference anchors firstly. The grasp rectangles are then regressed and classified based on these anchors. A new matching strategy for oriented anchors is proposed based on the rotation angles and center positions of the anchors. The well-known Cornell grasp dataset and Jacquard dataset are used to test the performance of the proposed method. Experimental results show that the proposed method can achieve higher grasp detection accuracy compared with other methods in the literature.