翻訳待ち：APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 19 Aug 2026] Title:APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs View a PDF of the paper titled APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs, by Bijan Kavousian and 4 other authors View PDF Abstract:Programming robots remains challenging for non-experts, as traditional methods require expert knowledge and even block-based interfaces often lack flexibility. Recent work has explored Large Language Models (LLMs) to automatically generate robot programs from natural language, but these systems remain limited by a lack of transparency, missing mechanisms to ensure alignment with user intent, and little support for reuse. We present APPROVE (AI-Powered Programming for Robots with Visual End-User Feedback), an LLM-based multi-modal end-user programming framework that integrates natural language input with a block-based interface and an explicit user confirmation step. Generated programs are visualized using a block-based interface in Blockly, allowing users to confirm, modify, or reject them before execution. Confirmed functions are stored in a library for reuse, gradually building a set of reliable program components. Our approach contributes a human-centered design for LLM-based robot programming that emphasizes user trust, intent alignment, and reusability. Comments: Accepted for publication in Procedia CIRP, Proceedings of the 20th CIRP Conference on Intelligent Computation in Manufacturing Engineering (ICME 2026) Subjects: Robotics (cs.RO); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.19281 [cs.RO] (or arXiv:2608.19281v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.19281 arXiv-issued DOI via DataCite (pending registration) Submission history From: Bijan Kavousian [view email] [v1] Wed, 19 Aug 2026 08:22:13 UTC (412 KB) Full-text links: Access Paper: View a PDF of the paper titled APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs, by Bijan Kavousian and 4 other authors View PDF view license Current browse context: cs.RO new | recent | 2026-08 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)