Difference-Triggered Issue Discovery by Maintaining Up-to-Date Environmental Understanding in Autonomous Robots

Difference-Triggered Issue Discovery by Maintaining Up-to-Date Environmental Understanding in Autonomous Robots | 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 Difference-Triggered Issue Discovery by Maintaining Up-to-Date Environmental Understanding in Autonomous Robots Hong Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9547966/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Autonomous robots operating in open and changing environments must discover emerging issues such as unfamiliar objects without relying on manually predefined issue types. However, many existing robotic learning and anomaly detection methods depend on fixed detection targets, static environmental representations, or direct recognition models, which limits their ability to discover newly appearing issues during long-term operation. This paper proposes a Difference-Triggered Issue Discovery with Up-to-Date Environmental Understanding (DTID-UEU) model for autonomous robots. The core idea is that a robot continuously maintains an internal understanding of the environment it has encountered, uses this understanding to predict expected observations, and compares the predictions with real-time observations. When the difference between the predicted and observed states becomes significant, the robot treats the inconsistency as a potential issue and activates further judgment, learning, and environmental understanding update. To support this process, DTID-UEU introduces a thinking-guided difference judgment mechanism to distinguish ordinary variation, noise, emerging issues, abnormal events, and task-relevant failures. It also employs difference-proportional learning, where meaningful observations with larger differences receive stronger learning intensity and are integrated into the robot's updated environmental understanding. Experimental results show that DTID-UEU achieves the highest F1-score and recall among the compared methods, improving the F1-score from 0.1710 to 0.3848 compared with the red-threshold baseline and from 0.0544 to 0.3848 compared with the static-understanding baseline. Autonomous robots continual learning environmental understanding difference-triggered learning open environments Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9547966","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630726951,"identity":"3640551c-5b03-41ab-8018-154a3ff02393","order_by":0,"name":"Hong Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBADOTb25gMk6TAw5uM5lkCalsR5EjkKxKmVn5F+TeLDnz/pbQw5DAw/KrYRYfyNnDLJmW0GuW0MZw8w9py5TYQWiZw0ad4GoBbGvgRmxjYitMjPAGr588cgnY2Zx4A4LQw30o9JM7AZJLCxEavF4MwbZsveNmPDNh62hINE+UW+Pf3hjR9/5OTl5z8++OBHBTEOY+AxgDMPEKMeCNgfEKlwFIyCUTAKRiwAAJtVOMUT72qXAAAAAElFTkSuQmCC","orcid":"","institution":"Chengdu University of Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2026-04-28 03:55:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9547966/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9547966/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182052,"identity":"2b914aba-5d24-4540-93e3-c6ac39647195","added_by":"auto","created_at":"2026-04-30 08:59:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":619237,"visible":true,"origin":"","legend":"","description":"","filename":"mv7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9547966/v1_covered_7c1b9a3f-f972-497d-8656-f7e18e9b1e43.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDifference-Triggered Issue Discovery by Maintaining Up-to-Date Environmental Understanding in Autonomous Robots\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autonomous robots, continual learning, environmental understanding, difference-triggered learning, open environments","lastPublishedDoi":"10.21203/rs.3.rs-9547966/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9547966/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutonomous robots operating in open and changing environments must discover emerging issues such as unfamiliar objects without relying on manually predefined issue types. 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