Enhancement of Long-Horizon Task Planning via Active and Passive Modification in Large Language Model

Enhancement of Long-Horizon Task Planning via Active and Passive Modification in Large Language Model | 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 Enhancement of Long-Horizon Task Planning via Active and Passive Modification in Large Language Model Kazuki Hori, Kanata Suzuki, Tetsuya Ogata This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4695355/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This study proposes a method for generating complex, long-horizon off-line task plans using large language models (LLMs). Although several studies have been conducted in recent years on robot task planning using LLMs, the planning results are often simple. In the proposed method, the LLM actively collects missing information by asking questions, and the task plan is upgraded with a one-shot dialog example. In addition, the robustness of the task planning is enhanced by using the method that modifies the plan results based on human instructions. The effectiveness of the proposed method is demonstrated through dialogue experiments using a cooking task as the subject. Furthermore, the proposed method can be applied to instructions that include images. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Dec, 2024 Reviews received at journal 16 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviews received at journal 16 Oct, 2024 Reviewers agreed at journal 16 Oct, 2024 Reviewers agreed at journal 16 Oct, 2024 Reviewers invited by journal 08 Oct, 2024 Editor assigned by journal 03 Oct, 2024 Editor invited by journal 11 Jul, 2024 Submission checks completed at journal 08 Jul, 2024 First submitted to journal 06 Jul, 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. 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