Deep Reinforcement Learning for Surgical Robotics with State and Image Information: A Survey

Deep Reinforcement Learning for Surgical Robotics with State and Image Information: A Survey | 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 Deep Reinforcement Learning for Surgical Robotics with State and Image Information: A Survey Edoardo Fazzari, Cesare Stefanini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8621244/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Surgical robotics has become a cornerstone of modern minimally invasive procedures, offering enhanced precision, dexterity, and ergonomics compared to conventional manual techniques. As the field progresses toward (semi-)autonomous operation, learning-based methods, particularly reinforcement learning (RL), have the potential to endow surgical robots with adaptable, data-driven decision-making capabilities. However, despite notable success of deep learning in tasks such as tool and anatomy segmentation, the application of RL to surgical robotics has remained largely confined to simplified tasks. As a result, the exploration of RL for realistic, high-fidelity surgical scenarios remains sparse, leaving substantial room for methodological and clinical advancement.This survey presents a comprehensive review of deep RL methods for surgical robotics across domains including laparoscopy, endoscopy, ophthalmology, and related specialties. We categorize existing policy-learning approaches into seven principal areas, analyzing their applications, effectiveness, and limitations. We additionally review the surgical environments and simulators that currently or may support RL research, highlighting the physical phenomena they model and the implications for downstream policy transfer. Public datasets relevant to RL agent training and surgical scene understanding are also summarized, emphasizing their role in enabling reproducible research and data-driven skill acquisition.We conclude by discussing open challenges and emerging research directions critical to advancing RL-driven autonomy in surgical robotics. Our goal is to provide a structured map of the field and a clear perspective on future opportunities toward safe, robust, and clinically meaningful autonomous surgical systems. Surgical Robotics Deep Reinforcement Learning Imitation Learning Robotics Deep Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 16 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-8621244","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578994958,"identity":"8b782e59-19be-41a9-9ffd-fcdd691652fc","order_by":0,"name":"Edoardo Fazzari","email":"data:image/png;base64,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","orcid":"","institution":"Mohamed bin Zayed University of Artificial Intelligence","correspondingAuthor":true,"prefix":"","firstName":"Edoardo","middleName":"","lastName":"Fazzari","suffix":""},{"id":578994963,"identity":"082cc38a-a02d-4729-8208-932404b7d3c8","order_by":1,"name":"Cesare Stefanini","email":"","orcid":"","institution":"Mohamed bin Zayed University of Artificial Intelligence","correspondingAuthor":false,"prefix":"","firstName":"Cesare","middleName":"","lastName":"Stefanini","suffix":""}],"badges":[],"createdAt":"2026-01-16 17:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8621244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8621244/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101012405,"identity":"eab646c2-51a4-4547-8285-3ad160ef02f8","added_by":"auto","created_at":"2026-01-23 20:11:29","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4401,"visible":true,"origin":"","legend":"","description":"","filename":"1674b7aa0e1c47f5b8beb98bc31e8afc.json","url":"https://assets-eu.researchsquare.com/files/rs-8621244/v1/b38505c5e4a61bdf4a47ffce.json"},{"id":101204152,"identity":"4c3241bd-f7e9-40b2-b4fa-0c92bce2baea","added_by":"auto","created_at":"2026-01-27 09:41:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":507104,"visible":true,"origin":"","legend":"","description":"","filename":"SURVEYDeepRLforSurgicalRobotics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8621244/v1_covered_543c3a07-ebe7-4ecc-bbb5-0de157192396.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Reinforcement Learning for Surgical Robotics with State and Image Information: A Survey","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"artificial-intelligence-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aire","sideBox":"Learn more about [Artificial Intelligence Review](http://link.springer.com/journal/10462)","snPcode":"10462","submissionUrl":"https://submission.nature.com/new-submission/10462/3","title":"Artificial Intelligence Review","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Surgical Robotics, Deep Reinforcement Learning, Imitation Learning, Robotics, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-8621244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8621244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Surgical robotics has become a cornerstone of modern minimally invasive procedures, offering enhanced precision, dexterity, and ergonomics compared to conventional manual techniques. As the field progresses toward (semi-)autonomous operation, learning-based methods, particularly reinforcement learning (RL), have the potential to endow surgical robots with adaptable, data-driven decision-making capabilities. However, despite notable success of deep learning in tasks such as tool and anatomy segmentation, the application of RL to surgical robotics has remained largely confined to simplified tasks. As a result, the exploration of RL for realistic, high-fidelity surgical scenarios remains sparse, leaving substantial room for methodological and clinical advancement.This survey presents a comprehensive review of deep RL methods for surgical robotics across domains including laparoscopy, endoscopy, ophthalmology, and related specialties. We categorize existing policy-learning approaches into seven principal areas, analyzing their applications, effectiveness, and limitations. 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