用专家经验指导的智能评审，让大模型写的机器人程序更安全可靠。
RoboCritics: Enabling Reliable End-to-End LLM Robot Programming through Expert-Informed Critics
- 引入专家规则审查机器人动作轨迹，识别越界、碰撞等风险
- 发现错误后提供一键修复建议，支持用户与大模型持续迭代优化
- 实测提升程序安全性与执行质量，适合无编程基础的用户使用
非专业用户虽可灵活重配机器人，但因缺乏机器人知识而面临挑战。大语言模型（LLMs）可通过自然语言降低编程门槛，但现有方法生成的代码如黑箱，难以验证和调试，带来实际系统中的安全与可靠性风险。本文提出RoboCritics，通过专家经验构建运动级评审机制，分析动作轨迹中的关节速度超限、碰撞及末端执行器不安全姿态等问题。一旦检测到违规，评审模块会提供透明反馈并支持一键修复，将结构化信息回传给LLM，实现用户参与下的迭代优化。我们在基于UR3e机器人的网页界面中实现该系统，并开展包含18名参与者的事先对照用户研究。相比基线LLM界面，RoboCritics显著减少安全违规，提升执行质量，并改变用户验证与修改程序的方式。结果表明，RoboCritics能实现更可靠、以用户为中心的端到端机器人编程。
原文摘要 · Abstract (English)
End-user robot programming grants users the flexibility to re-task robots in situ, yet it remains challenging for novices due to the need for specialized robotics knowledge. Large Language Models (LLMs) hold the potential to lower the barrier to robot programming by enabling task specification through natural language. However, current LLM-based approaches generate opaque, "black-box" code that is difficult to verify or debug, creating tangible safety and reliability risks in physical systems. We present RoboCritics, an approach that augments LLM-based robot programming with expert-informed motion-level critics. These critics encode robotics expertise to analyze motion-level execution traces for issues such as joint speed violations, collisions, and unsafe end-effector poses. When violations are detected, critics surface transparent feedback and offer one-click fixes that forward structured messages back to the LLM, enabling iterative refinement while keeping users in the loop. We instantiated RoboCritics in a web-based interface connected to a UR3e robot and evaluated it in a between-subjects user study (n=18). Compared to a baseline LLM interface, RoboCritics reduced safety violations, improved execution quality, and shaped how participants verified and refined their programs. Our findings demonstrate that RoboCritics enables more reliable and user-centered end-to-end robot programming with LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。