让普通人通过对话和可视化块编程控制机器人，还能复用成功指令。
APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs
- 结合自然语言与可视化编程块，用户可直观确认或修改生成的程序。
- 支持用户反馈确认，确保程序符合真实意图，提升可信度。
- 生成的正确代码存入库，方便后续重复使用，降低编程门槛。
非专业人士编程机器人仍面临挑战，传统方法需专业知识，即使采用积木式界面也缺乏灵活性。近期研究利用大语言模型（LLMs）从自然语言自动生成机器人程序，但这些系统存在透明性不足、难以保证与用户意图对齐、复用性差等问题。本文提出APPROVE（AI驱动的带视觉用户反馈的机器人编程），一个基于LLM的多模态用户编程框架，融合自然语言输入、积木式界面（Blockly）和显式的用户确认步骤。生成的程序以积木形式可视化展示，用户可在执行前确认、修改或拒绝。确认后的功能模块被存入库中供复用，逐步构建可靠的程序组件集。该方法强调以用户为中心的设计，注重用户信任、意图对齐与可复用性。
原文摘要 · Abstract (English)
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.
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