Cognitive Embodied Learning for Anomaly Active Target Tracking

Cognitive Embodied Learning for Anomaly Active Target Tracking | 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 Cognitive Embodied Learning for Anomaly Active Target Tracking Fuhui Zhou, Qihui Wu, Jiahao Li, Jiahuan Ji, Haoyang Wang, Hongtao Liang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5789601/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Communications Engineering → Version 1 posted You are reading this latest preprint version Abstract The primary challenge in active object tracking (AOT) lies in maintaining robust and accurate tracking performance in the complex physical scenarios. Existing end-to-end frameworks based on deep learning and reinforcement learning often struggle with high computational costs, data dependency, and limited generalization, hindering their performance in practical applications. Although embodied intelligence (EI) is promising to enable agents to learn from physical interactions, it cannot tackle severe anomalies happened in the complex scenarios. In order to address this issue, we propose a novel embodied learning method, called the Cognitive Embodied Learning (CEL), which is inspired by the dual decisionmaking system of the human brain. The CEL can dynamically switch between normal tracking and anomaly handling modes, supported by specialized modules including the anomaly cognition module (ACM), the rule reasoning module (RRM), and the anomaly elimination module (AEM). Moreover, we further introduce the categorical objective function (COF) to address function non-measurability and data confusion caused by severe anomalies. Extensive unmanned aerial vehicle (UAV) anomaly active target tracking experiments in both simulated and real-world scenarios demonstrate the superior performance of our method. Compared to the state-of-the-art methods, the CEL achieves a 361.4% increase in the success rate and a 54.4% improvement of the task completion efficiency, which highlights the potential of CEL to advance the field of AOT and open new avenues for more robust and intelligent tracking systems in the challenging environments. Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationofCognitiveEmbodiedLearningforAnomalyActiveTargetTracking.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Communications Engineering → 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-5789601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":406800938,"identity":"5e3123a5-348b-4e99-9743-9475de2e2f15","order_by":0,"name":"Fuhui Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCSCuYDjAwMbe2ECCljMgLTwHSdXCIJFApA752c3HJA623Unsk3zc+OBDBYOcef8C/FoY5xxLA2p5ltgmndhsOOMMg7HMjQf4tTBL5JhJf2w7DNLSJs3bxpA4Q+IAfi1sQC1AW4BaJA+2//77jwgtPHAtEoxtzIwNQC38Dfi1SEikJVscOHfYuI0nsVmy55iEsYQEfh0M8jOSD944UHZYdn778YcfftTYyEnwE3AYhq0kRBACkGrLKBgFo2AUDHsAAGxTRwn/H4OyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6880-6244","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":true,"prefix":"","firstName":"Fuhui","middleName":"","lastName":"Zhou","suffix":""},{"id":406800939,"identity":"bb2831ff-13a0-405d-963a-39a4b7bd58ee","order_by":1,"name":"Qihui Wu","email":"","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":false,"prefix":"","firstName":"Qihui","middleName":"","lastName":"Wu","suffix":""},{"id":406800940,"identity":"ab8f9885-50fd-4f51-b0e0-89499bf4f015","order_by":2,"name":"Jiahao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Li","suffix":""},{"id":406800941,"identity":"cfa7ebe6-62e2-410d-bc66-c66c90a064cf","order_by":3,"name":"Jiahuan Ji","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiahuan","middleName":"","lastName":"Ji","suffix":""},{"id":406800942,"identity":"01420c6a-8077-450b-88ce-199534c16407","order_by":4,"name":"Haoyang Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Haoyang","middleName":"","lastName":"Wang","suffix":""},{"id":406800943,"identity":"ab8f9ea9-2a93-4b3b-b4c1-6f49de84da70","order_by":5,"name":"Hongtao Liang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hongtao","middleName":"","lastName":"Liang","suffix":""},{"id":406800944,"identity":"e11e34c7-955e-4aee-8650-3e152fd476d6","order_by":6,"name":"Kai-Kuang Ma","email":"","orcid":"","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":false,"prefix":"","firstName":"Kai-Kuang","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-01-08 13:51:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5789601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5789601/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s44172-025-00556-6","type":"published","date":"2025-11-27T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99211940,"identity":"b420e839-8e55-48a7-b1b2-9b3868d44ff1","added_by":"auto","created_at":"2025-12-30 08:23:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4110593,"visible":true,"origin":"","legend":"","description":"","filename":"CognitiveEmbodiedLearningforAnomalyActiveTargetTracking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5789601/v1_covered_8d372fa6-6995-4f73-a23f-49e23cabf907.pdf"},{"id":74935671,"identity":"3886eacd-f4b7-46ea-8084-b59de51708c1","added_by":"auto","created_at":"2025-01-28 13:18:35","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7236585,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationofCognitiveEmbodiedLearningforAnomalyActiveTargetTracking.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5789601/v1/c098923b869f33a90c4ccdb2.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Cognitive Embodied Learning for Anomaly Active Target Tracking","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5789601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5789601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe primary challenge in active object tracking (AOT) lies in maintaining robust and accurate tracking performance in the complex physical scenarios. Existing end-to-end frameworks based on deep learning and reinforcement learning often struggle with high computational costs, data dependency, and limited generalization, hindering their performance in practical applications. Although embodied intelligence (EI) is promising to enable agents to learn from physical interactions, it cannot tackle severe anomalies happened in the complex scenarios. In order to address this issue, we propose a novel embodied learning method, called the Cognitive Embodied Learning (CEL), which is inspired by the dual decisionmaking system of the human brain. The CEL can dynamically switch between normal tracking and anomaly handling modes, supported by specialized modules including the anomaly cognition module (ACM), the rule reasoning module (RRM), and the anomaly elimination module (AEM). Moreover, we further introduce the categorical objective function (COF) to address function non-measurability and data confusion caused by severe anomalies. Extensive unmanned aerial vehicle (UAV) anomaly active target tracking experiments in both simulated and real-world scenarios demonstrate the superior performance of our method. Compared to the state-of-the-art methods, the CEL achieves a 361.4% increase in the success rate and a 54.4% improvement of the task completion efficiency, which highlights the potential of CEL to advance the field of AOT and open new avenues for more robust and intelligent tracking systems in the challenging environments.\u003c/p\u003e","manuscriptTitle":"Cognitive Embodied Learning for Anomaly Active Target Tracking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 13:18:31","doi":"10.21203/rs.3.rs-5789601/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-engineering","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commseng","sideBox":"Learn more about [Communications Engineering](http://link.springer.com/journal/44172)","snPcode":"44172","submissionUrl":"https://mts-commseng.nature.com/cgi-bin/main.plex","title":"Communications Engineering","twitterHandle":"@commseng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4fc36e1c-2b90-4fe2-9deb-8c216b62feed","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T08:22:56+00:00","versionOfRecord":{"articleIdentity":"rs-5789601","link":"https://doi.org/10.1038/s44172-025-00556-6","journal":{"identity":"communications-engineering","isVorOnly":false,"title":"Communications Engineering"},"publishedOn":"2025-11-27 05:00:00","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-01-28 13:18:31","video":"","vorDoi":"10.1038/s44172-025-00556-6","vorDoiUrl":"https://doi.org/10.1038/s44172-025-00556-6","workflowStages":[]},"version":"v1","identity":"rs-5789601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5789601","identity":"rs-5789601","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}