Meta-Domain Adaptive Transfer (MDAT) Learning for Rapid Adaptation in Simulated Robotic Tasks

Meta-Domain Adaptive Transfer (MDAT) Learning for Rapid Adaptation in Simulated Robotic Tasks | 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 Meta-Domain Adaptive Transfer (MDAT) Learning for Rapid Adaptation in Simulated Robotic Tasks Yu Nong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5806840/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 This paper introduces Meta-Domain Adaptive Transfer (MDAT), a novel approach combining meta-learning, adaptive domain randomization, and feature disentanglement for rapid adaptation in simulated robotic tasks. We evaluate MDAT across three distinct robotic scenarios: object manipulation, navigation, and bipedal locomotion. Our results demonstrate significant improvements in adaptation speed, task performance, and generalization capabilities compared to existing methods such as MAML, Traditional Transfer Learning (TTL), Domain Randomization (DR), and Progressive Neural Networks (PNN). 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. 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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-5806840","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":400703958,"identity":"dca37840-ced9-4d15-a70a-f9a3d9185e97","order_by":0,"name":"Yu Nong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBADOQZmMG3BwAamC5gJajEGamFsYGCQgGoxIKwlEagcooWBkBaDGwlsDz7uqE3fcJz3+WOeGgl5PgYew8cVBtb4tLAbzjxzPHfDYXbDZp5jEoZtDDzGhmcM0vHaIs3bdix3ZjMbYzMPmwRjGwPvNskGg8MEtaRLgrX8k7AnVktNAj8zUAtvm0QiQS2SZx62Sc5sO2DYD9Qyc26fRHIbM/9nwwY8fuE7nnxM4mNbnTwb/zGGD2++2djOb29LfNhQgTvEFA6AYoMB2RmEIlG+AUzVEVA2CkbBKBgFIxoAAFCASkBMPJDYAAAAAElFTkSuQmCC","orcid":"","institution":"VirtualAI China","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Nong","suffix":""}],"badges":[],"createdAt":"2025-01-11 02:12:32","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-5806840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5806840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73720356,"identity":"fd25a611-cec8-47ce-8edc-c0216641a00a","added_by":"auto","created_at":"2025-01-14 02:25:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":306750,"visible":true,"origin":"","legend":"","description":"","filename":"mdatresearch.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5806840/v1_covered_734ee3ff-a783-4fbd-96a2-2e9787dfeb78.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMeta-Domain Adaptive Transfer (MDAT) Learning for Rapid Adaptation in Simulated Robotic Tasks\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":"","lastPublishedDoi":"10.21203/rs.3.rs-5806840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5806840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces Meta-Domain Adaptive Transfer (MDAT), a novel approach combining meta-learning, adaptive domain randomization, and feature disentanglement for rapid adaptation in simulated robotic tasks. 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