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NG-Net: No-Grasp annotation grasp detection network for stacked scenes

Author

Listed:
  • Min Shi

    (North China Electric Power University)

  • Jingzhao Hou

    (North China Electric Power University)

  • Zhaoxin Li

    (Chinese Academy of Agricultural Sciences)

  • Dengming Zhu

    (Chinese Academy of Sciences)

Abstract

Achieving a high grasping success rate in a stacked environment is the core of the robot’s grasping task. Most methods achieve a high grasping success rate by training the network on a dataset containing a large number of grasping annotations which requires a lot of manpower and material resources. Therefore, achieving a high grasping success rate for stacked scenes without grasping annotations is a challenging task. To address this, we propose a No-Grasp annotation grasp detection network for stacked scenes (NG-Net). Our network consists of two modules: an object selection module and a grasp generation module. Specifically, the object selection module performs instance segmentation on the raw point cloud to select the object with the highest score as the object to be grasped, and the grasp generation module uses mathematical methods to analyze the geometric features of the point cloud surface to achieve grasping pose generation without grasping annotations. Experiments show that on the modified IPA-Binpicking dataset G, NG-Net has an average grasp success rate of 97% in the stacked scene grasp experiment, 14–22% higher than PointNetGPD.

Suggested Citation

  • Min Shi & Jingzhao Hou & Zhaoxin Li & Dengming Zhu, 2025. "NG-Net: No-Grasp annotation grasp detection network for stacked scenes," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1477-1490, February.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-024-02321-6
    DOI: 10.1007/s10845-024-02321-6
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    References listed on IDEAS

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    1. Xiaoqian Huang & Mohamad Halwani & Rajkumar Muthusamy & Abdulla Ayyad & Dewald Swart & Lakmal Seneviratne & Dongming Gan & Yahya Zweiri, 2022. "Real-time grasping strategies using event camera," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 593-615, February.
    2. Weifei Hu & Jinyi Shao & Qing Jiao & Chuxuan Wang & Jin Cheng & Zhenyu Liu & Jianrong Tan, 2023. "A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2943-2961, October.
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