SA-Grasp: A Self-Attention mechanism based lightweight grabbing pose detection network

SA-Grasp: A Self-Attention mechanism based lightweight grabbing pose detection network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SA-Grasp: A Self-Attention mechanism based lightweight grabbing pose detection network Quan-cheng Pu, Hui Zhang, Lu Yang, Tie-shan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6912803/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in International Journal of Intelligent Robotics and Applications → Version 1 posted 9 You are reading this latest preprint version Abstract In response to the current convolutional neural network-based robotic arm grasping pose detection networks being susceptible to interference from redundant information, and the tendency to misjudge object contour areas as grasping execution positions, 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 grasping 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. Grasping pose detection Neural networks Plane grasping Self-attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2026 Read the published version in International Journal of Intelligent Robotics and Applications → Version 1 posted Editorial decision: Revision requested 10 Aug, 2025 Reviews received at journal 30 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 17 Jun, 2025 Submission checks completed at journal 17 Jun, 2025 First submitted to journal 17 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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