Journal of Computer Science and Technology


Accurate Robotic Grasp Detection with Angular Label Smoothing

Min Shi1 (石敏), Member, CCF, Hao Lu1 (路昊), Zhao-Xin Li2,* (李兆歆), Member, CCF, Deng-Ming Zhu2 (朱登明), Member, CCF, and Zhao-Qi Wang2 (王兆其), Member, CCF, IEEE   

  1. 1School of Control and Computer Engineering, North China Electric Power University, Beijing 1002206, China
    2Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Contact: Zhao-Xin Li
  • About author:Zhao-Xin Li received the B.Sc. degree and M.Sc. degree in computer science from Tianjin Polytechnic University, Tianjin, in 2007 and 2010, respectively, and received the Ph.D. degree in computer application technology from Harbin Institute of Technology, Harbin, in 2016. From September 2018 to March 2019, he worked as a Postdoctoral Fellow in the Department of Computing, The Hong Kong Polytechnic University. He is currently with the Institute of Computing Technology, Chinese Academy of Sciences, China. His research interests include 3D computer vision and 3D data processing.

Grasp detection is a visual recognition problem where the robot makes use of its sensors to detect graspable objects in its environment. Despite the steady progress in robotic grasping, it is still difficult for the existing methods to achieve both real-time and high accuracy grasping detection. In this paper, we propose a real-time robotic grasp detection method, which can accurately predict potential grasp for parallel-plate robotic gripper using RGB images. Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector, and for the first time, we add an attention mechanism to the grasp detection task, which enables the network to focus on grasp regions rather than background. Specifically, we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network. We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset. Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods. In particular, our grasp detection method ranked No.1 on both the Cornell dataset and the Jacquard dataset, giving rise to accuracy of 98.9% and 95.6% respectively at real-time calculation speed.


我们提出了一种用于平面抓取预测的新型抓取检测网络,可以实现实时准确地从 RGB 图像中预测五维抓取框。我们采用由特征提取器和抓握检测器组成的端到端卷积神经网络,并且首次将注意力机制引入抓握检测任务中。同时,针对抓取角度信息的学习我们提出了一种用于抓取角度分类的角度标签平滑策略。
4、结果(Result & Findings):
在实时性能方面,我们的模型在 Cornell 数据集和 Jacquard 数据集上分别达到了98.9%和95.6%的准确率,实现了优于目前最先进方法的性能。同时,在实际场景的大量实验表明了所提出的方法具有卓越的性能。

Key words: robotic grasp detection; attention mechanism; angular label smoothing; anchor box; deep learning in robotic grasping;

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