A Bayesian Framework for Learning Proactive Robot Behaviour in Assistive Tasks

A Bayesian Framework for Learning Proactive Robot Behaviour in Assistive 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 A Bayesian Framework for Learning Proactive Robot Behaviour in Assistive Tasks Antonio Andriella, Ilenia Cucciniello, Antonio Origlia, Silvia Rossi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3845717/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2024 Read the published version in User Modeling and User-Adapted Interaction → Version 1 posted 7 You are reading this latest preprint version Abstract Socially assistive robots represent a promising tool in assistive contexts to improve people's quality of life and well-being through social and emotional support, just like cognitive or physical. However, the effectiveness of interactions depends significantly on their ability to adapt to the needs of the assisted persons and act proactively in an anticipatory way, offering assistance before it is explicitly requested. Unfortunately, most of the previous work has only focused on what actions the robot should perform, rather than considering when to act and how confident it should be in a given situation. To address this gap, in this paper, we introduce a new data-driven framework that involves the use of a learning pipeline, consisting of 2 phases, with the ultimate goal of training an algorithm based on Influence Diagrams. The assistance scenario considered involves a sequential memory game where the robot learns autonomously what assistance to provide, when and with what confidence to take control and intervene. The results obtained from a user study showed that the proactive behaviour of the robot had a positive impact on the users' game performance. They obtained higher scores, made fewer mistakes, and requested less assistance from the robot. The study also highlighted the robot's ability to provide assistance tailored to users' specific needs and to anticipate their requests. Assistive Robotics Proactive Behaviour Influence Diagrams Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2024 Read the published version in User Modeling and User-Adapted Interaction → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Reviews received at journal 17 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers invited by journal 10 Jan, 2024 Editor assigned by journal 09 Jan, 2024 Submission checks completed at journal 09 Jan, 2024 First submitted to journal 08 Jan, 2024 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-3845717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266227027,"identity":"f484274e-0813-473e-a77d-68bb3e66ace6","order_by":0,"name":"Antonio Andriella","email":"","orcid":"","institution":"PAL Robotics (Spain)","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Andriella","suffix":""},{"id":266227028,"identity":"413a1196-c483-4275-8bf1-9a6d3bb03bb1","order_by":1,"name":"Ilenia Cucciniello","email":"","orcid":"","institution":"University of Naples Federico II","correspondingAuthor":false,"prefix":"","firstName":"Ilenia","middleName":"","lastName":"Cucciniello","suffix":""},{"id":266227029,"identity":"14ce0174-1fa7-4053-bf5b-26c058204985","order_by":2,"name":"Antonio Origlia","email":"","orcid":"","institution":"University of Naples Federico II","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Origlia","suffix":""},{"id":266227030,"identity":"42840351-12b0-4342-9f7c-0fc8052ddd9b","order_by":3,"name":"Silvia Rossi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3RvQrCMBDA8QsFXQKuFYS+wm1SUHyViKviJg4OFaEuRVd9m5QDXcSuHRxaBOdO0klM/eyUgpND/tNl+MElATCZ/jHL8kC8ZpkB8MegJ+xLws2b6I0iH87fk444S+bbyQzGjcY+pa5/arXrklGmIUiKiB24280QaeRfuBsI/WJosQWKGiDGHBQhjrKCOIuC3BSJ9gm5BYmSiusTmyd9XxEpiiUViasWe5CVjU11lzA4FiT15EG32JpkmF87qF7snOUT6mE0oGyqW+yZXT6UfspkMplMv3UHYMlTvCud8/QAAAAASUVORK5CYII=","orcid":"","institution":"University of Naples Federico II","correspondingAuthor":true,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Rossi","suffix":""}],"badges":[],"createdAt":"2024-01-08 15:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3845717/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3845717/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11257-024-09421-1","type":"published","date":"2024-12-26T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72640520,"identity":"3d116ff0-5c08-4bbf-8612-6d60efa3dd05","added_by":"auto","created_at":"2024-12-30 16:06:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243895,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845717/v1_covered_7e4f723d-f9b9-47c1-ba37-80e0449358ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Bayesian Framework for Learning Proactive Robot Behaviour in Assistive Tasks","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":"user-modeling-and-user-adapted-interaction","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"umui","sideBox":"Learn more about [User Modeling and User-Adapted Interaction](http://link.springer.com/journal/11257)","snPcode":"11257","submissionUrl":"https://submission.nature.com/new-submission/11257/3","title":"User Modeling and User-Adapted Interaction","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Assistive Robotics, Proactive Behaviour, Influence Diagrams","lastPublishedDoi":"10.21203/rs.3.rs-3845717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3845717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Socially assistive robots represent a promising tool in assistive contexts to improve people's quality of life and well-being through social and emotional support, just like cognitive or physical. 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