Safety limitations in service robotics across various industries have raised significant concerns about the need for robust mechanisms ensuring that robots adhere to safe practices, thereby preventing actions that might harm humans or cause property damage. Despite advances, including the integration of Knowledge Graphs (KGs) with Large Language Models (LLMs), challenges in ensuring consistent safety in autonomous robot actions persist. In this paper, we propose a novel integration of Large Language Models with Embodied Robotic Control Prompts (ERCPs) and Embodied Knowledge Graphs (EKGs) to enhance the safety framework for service robots. ERCPs are designed as predefined instructions that ensure LLMs generate safe and precise responses. These responses are subsequently validated by EKGs, which provide a comprehensive knowledge base ensuring that the actions of the robot are continuously aligned with safety protocols, thereby promoting safer operational practices in varied contexts. Our experimental setup involved diverse real-world tasks, where robots equipped with our framework demonstrated significantly higher compliance with safety standards compared to traditional methods. This integration fosters secure human-robot interactions and positions our methodology at the forefront of AI-driven safety innovations in service robotics.

翻译：各行业服务机器人的安全局限性已引发对稳健机制的迫切需求，以确保机器人遵循安全规范，从而防止可能伤害人类或造成财产损失的行为。尽管已有包括知识图谱与大型语言模型融合在内的技术进步，但在确保自主机器人行为持续安全方面仍存在挑战。本文提出一种将大型语言模型与具身机器人控制提示及具身知识图谱相结合的新颖框架，以增强服务机器人的安全体系。具身机器人控制提示被设计为预定义指令，确保大型语言模型生成安全且精确的响应。这些响应随后由具身知识图谱进行验证，该图谱提供全面的知识库，确保机器人的行为持续符合安全协议，从而在多样化场景中推动更安全的操作实践。我们的实验设置涵盖多种现实世界任务，装备本框架的机器人相较于传统方法，在安全标准符合度上表现出显著提升。该融合方案促进了安全的人机交互，并将本方法置于服务机器人领域人工智能驱动安全创新的前沿。