{"paper_id":"3f47c646-e5c8-44ef-832e-e11fdc97d82d","body_text":"Agile Assistive Hospital Robot for Suboptimal Task Execution in Dynamic Environments | 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 Agile Assistive Hospital Robot for Suboptimal Task Execution in Dynamic Environments Yun-Chi Chiang, I-Pei Lee, Li-Chen Fu, Yun-Hsiang Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7764373/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Healthcare staff shortages increase workloads and create a need for assistive robots capable of handling routine hospital tasks. Traditional rule-based systems struggle in dynamic environments and with unforeseen requests. We present an agile hospital assistive robot that interprets natural language instructions using an AI-based task planner and keyword retrieval to generate executable task sequences. The robot adapts in real time to additional user requests, reschedules tasks using a suboptimal genetic-algorithm-based approach, and recovers from execution failures with vision-language reasoning and AI suggestions. Deployed on a Temi robot with a custom Android application, the system demonstrates effective handling of dynamic changes, improved human-robot interaction efficiency, and positive feedback from nursing staff. Socially assistive robots hostpital robots task planning suboptimal rescheduling large language models human-robot interaction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviews received at journal 09 Nov, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 04 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 02 Oct, 2025 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-7764373\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":528159951,\"identity\":\"98f16584-a3fe-4aa2-aa62-7ae001cd5faf\",\"order_by\":0,\"name\":\"Yun-Chi 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Traditional rule-based systems struggle in dynamic environments and with unforeseen requests. We present an agile hospital assistive robot that interprets natural language instructions using an AI-based task planner and keyword retrieval to generate executable task sequences. The robot adapts in real time to additional user requests, reschedules tasks using a suboptimal genetic-algorithm-based approach, and recovers from execution failures with vision-language reasoning and AI suggestions. 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