SAGE: Decoupling Spatial Logic from Metric Scale for Zero-Shot Multi-Robot Exploration

SAGE: Decoupling Spatial Logic from Metric Scale for Zero-Shot Multi-Robot Exploration | 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 Article SAGE: Decoupling Spatial Logic from Metric Scale for Zero-Shot Multi-Robot Exploration Peng Lu, Xiao Cao, Mingyang Li, Yi Luo, Yuting Tao, Bowen Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8307666/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Effective multi-robot exploration in unknown environments faces a critical structural dilemma: high-level coordination typically relies on global spatial priors or fixed-scale representations, yet real-world missions operate in unbounded, irregular domains where such priors are unavailable. Here, we present SAGE, a framework that resolves this dilemma by treating exploration as a topological invariant, independent of metric boundaries. SAGE acts as a dimensional reduction operator, abstracting high-dimensional sensor streams into a sparse, dynamic graph that decouples decision logic from physical scale. To stabilize learning on these continuously growing structures, we introduce a capacity-controlled multi-agent reinforcement learning regime. By synchronizing the agent's observable horizon with its effective action space via a dynamic masking protocol, this mechanism constrains the complexity of the growing environment, ensuring the policy masters local topological correlations. We demonstrate that policies trained exclusively on minimalist, structured 60 × 60 grid worlds transfer zero-shot to environments of vastly different physics and scales—from large-scale unstructured maps to real-world deployment in dense, irregular forests. These results verify that abstracting spatial logic from metric geometry enables robust, boundless exploration without environment-specific tuning. Physical sciences/Engineering/Mechanical engineering Physical sciences/Engineering/Aerospace engineering Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.pdf Supplementary Information finalmark1nc.mp4 Supplementary Movie 1 Cite Share Download PDF Status: Under Review Version 1 posted 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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