APPROVE: 視覚的エンドユーザー参加型LLMロボットプログラミング
APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs
LLMが生成したロボットプログラムをブロック型インターフェースで可視化し、ユーザーが確認・修正・再利用できるフレームワークを提案した論文。
著者: Bijan Kavousian, Miray Özakkas, Josefine Monnet, Oliver Petrovic, Christian Brecher
分類: cs.RO, cs.HC
原文アブストラクト
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.