Efficient Robot Navigation in Dense Crowds via Causal Reasoning on Spatio-Temporal Graphs

Efficient Robot Navigation in Dense Crowds via Causal Reasoning on Spatio-Temporal Graphs | 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 Efficient Robot Navigation in Dense Crowds via Causal Reasoning on Spatio-Temporal Graphs Yuhan Wang, Bo Liu, Liming Zhang, Yong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8600622/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract As mobile robot technology advances, safe and efficient navigation in dynamic crowd environments has become a major challenge. Traditional methods often overlook complex interactions between agents and pedestrians, limiting their dynamic obstacle avoidance in dense crowds. Recent methods using deep reinforcement learning and graph convolutional networks have improved robot navigation, but they focus mainly on spatial interactions and neglect temporal dependencies in crowd dynamics. We design a dense crowd navigation method using spatio-temporal graph reasoning, which constructs a agent-pedestrian interaction graph and applies an interaction-aware graph convolutional network to extract spatial features. We propose a Causal Spatio-Temporal Graph Attention Mechanism that models the temporal dependencies of pedestrian interaction dynamics and captures the causal relationships between an agent's state space and its actions. For multi-step spatio-temporal graphs, we extract features using a multi-layer, multi-head attention architecture to effectively encode their temporal structure. This approach yields more accurate predictions of pedestrian motion trends and improves the robot's obstacle-avoidance performance in complex crowd Experimental results show our method outperforms traditional methods in dense crowd scenarios. spatio-temporal graph reasoning robot navigation causal reasoning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 May, 2026 Reviews received at journal 09 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 15 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 14 Jan, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8600622","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575380911,"identity":"da81958e-1745-4693-80dc-3bb8f8280f09","order_by":0,"name":"Yuhan Wang","email":"","orcid":"","institution":"Nanjing University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Wang","suffix":""},{"id":575380912,"identity":"3992fb25-afb3-4e5c-807f-9ee7dc33e560","order_by":1,"name":"Bo Liu","email":"","orcid":"","institution":"Nanjing University of Science and 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