APPROVE：结合大型语言模型的面向视觉端用户的机器人编程
APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs
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中文总结 AI 辅助
APPROVE是一种结合LLMs的多模态端用户机器人编程框架，通过Blockly可视化程序并支持用户确认、修改或拒绝，还可存储复用已确认功能，解决了现有系统透明度不足、意图对齐差、复用有限的问题，提升了用户信任与编程灵活性。
中文摘要 AI 辅助
机器人编程对非专业人士而言仍具挑战性，传统方法需要专业知识，即便是基于块的界面也往往缺乏灵活性。近期研究探索用大型语言模型（LLMs）从自然语言自动生成机器人程序，但这些系统存在透明度不足、缺乏确保与用户意图对齐的机制、复用支持有限等局限。本文提出APPROVE（AI-Powered Programming for Robots with Visual End-User Feedback，即结合视觉端用户反馈的机器人AI驱动编程），这是一种基于LLMs的多模态端用户编程框架，将自然语言输入与基于块的界面及明确的用户确认步骤相集成。生成的程序在Blockly的基于块界面中可视化，用户可在执行前确认、修改或拒绝；已确认的功能会被存储在库中以便复用，逐步构建出一组可靠的程序组件。该方法为基于LLMs的机器人编程提供了以人为本的设计，强调用户信任、意图对齐与可复用性。
英文摘要
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.
