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<p>In response to the current convolutional neural network-based robotic arm grasp pose detection networks being susceptible to interference from redundant information, and the tendency to misjudge object contour areas as grasping execution poses , leading to low grasping success rates of robotic arms, this paper introduces SA-Grasp, a lightweight convolutional neural network for robotic arm grasping that integrates self-attention mechanism. It reduces interference from redundant information and improves grasping accuracy. SA-Grasp achieved high detection accuracies of 98.37% on Cornell and 96.33% on Jacquard datasets, with a fast detection time of 21 ms per image. In real-world tests, it demonstrated a 94.44% success rate across 180 grasping attempts on 9 unknown objects, demonstrating its reliability. SA-Grasp is a lightweight visual grasp pose detection network characterized by high grasping success rates and fast speeds, providing a new approach for the application of self-attention mechanism in the field of robotic arm visual grasping.</p>

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SA-Grasp: A self-attention mechanism based lightweight grasp pose detection network

  • 期刊论文

  • 发表日期：

    2026年3月31日

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  • Quan-cheng Pu,
  • Hui Zhang,
  • Lu Yang,
  • Tie-shan Li

摘要

In response to the current convolutional neural network-based robotic arm grasp pose detection networks being susceptible to interference from redundant information, and the tendency to misjudge object contour areas as grasping execution poses , leading to low grasping success rates of robotic arms, this paper introduces SA-Grasp, a lightweight convolutional neural network for robotic arm grasping that integrates self-attention mechanism. It reduces interference from redundant information and improves grasping accuracy. SA-Grasp achieved high detection accuracies of 98.37% on Cornell and 96.33% on Jacquard datasets, with a fast detection time of 21 ms per image. In real-world tests, it demonstrated a 94.44% success rate across 180 grasping attempts on 9 unknown objects, demonstrating its reliability. SA-Grasp is a lightweight visual grasp pose detection network characterized by high grasping success rates and fast speeds, providing a new approach for the application of self-attention mechanism in the field of robotic arm visual grasping.