SKGNet: Robotic Grasp Detection With Selective Kernel Convolution

Abstract

Real-time and accuracy are important evaluation metrics of robotic grasp detection algorithms. To further improve the accuracy on the premise of ensuring real-time performance, in this paper, a new Selective Kernel convolution Grasp detection Network (SKGNet) is proposed. Compared with previous methods, the attention mechanism and multi-scale fusion features are integrated into the SKGNet, which makes the network not only pay full attention to the grasp area but also flexibly adjust the grasp area according to the scale of the object, thus effectively distinguishing the object from the background. The SKGNet is trained and tested on the Cornell dataset and the Jacquard dataset, with the accuracy of 99.1% and 95.9% respectively, which is superior to SOTA methods. Moreover, SKGNet’s detection speed has reached 28fps. To demonstrate the performance of SKGNet, comparison studies and ablation experiments are performed in this paper. Finally, the grasp experiments of Baxter robot are also performed to verify the generalization of SKGNet in the actual scene, which achieves an average grasping success rate of 96.5%. Video is available at https://youtu.be/j07sb_ChzWQ. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Autonomous grasping is an very important skill for the robotic systems in the real world. However, due to the low grasp detection accuracy, robotic grasp is still a challenging problem. Although some methods have been developed to improve the grasp detection accuracy, the time efficiency is poor. Grasp detection with good accuracy and efficiency is worthy of further study. In this view, a novel deep learning-based grasp detection network SKGNet is proposed in this paper. It takes RGB-D images as input, trains and tests on public datasets, and finally outputs a series of grasp rectangles. Compared with the existing works, it not only achieves state-of-the-art detection accuracy, but also has high efficiency. To demonstrate the generalization performance and effectiveness, the SKGNet is also tested in the real world, and applied to perform the actual grasp task of Baxter robot. The results show that the SKGNet has good robustness and can detect the unknown objects of different sizes and shapes in the real world well.