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Regular Paper

Accurate Robotic Grasp Detection with Angular Label Smoothing

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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Abstract

Grasp detection is a visual recognition task 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 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 grippers 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 first on both the Cornell dataset and the Jacquard dataset, giving rise to the accuracy of 98.9% and 95.6%, respectively at real-time calculation speed.

Keywords

robotic grasp detectionattention mechanismangular label smoothinganchor boxdeep learning

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Journal of Computer Science and Technology
Volume 38 Issue 5,
September 2023
Pages 1149-1161
DOI: 10.1007/s11390-022-1458-5
Cite this article:
Shi M, Lu H, Li Z-X, et al. Accurate Robotic Grasp Detection with Angular Label Smoothing. Journal of Computer Science and Technology, 2023, 38(5): 1149-1161. https://doi.org/10.1007/s11390-022-1458-5

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Received: 22 March 2021
Accepted: 14 August 2022
Published: 30 September 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
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