Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced by Large Language Models (LLMs), which enable robots to better understand complex language commands and perform advanced tasks with enhanced comprehension and adaptability, highlighting their potential to improve embodied AI capabilities. However, this advancement also introduces safety challenges, particularly in robotic navigation tasks. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections, resulting in unsafe behaviours such as detours or collisions. To address these issues, we propose \textit{SafeEmbodAI}, a safety framework for integrating mobile robots into embodied AI systems. \textit{SafeEmbodAI} incorporates secure prompting, state management, and safety validation mechanisms to secure and assist LLMs in reasoning through multi-modal data and validating responses. We designed a metric to evaluate mission-oriented exploration, and evaluations in simulated environments demonstrate that our framework effectively mitigates threats from malicious commands and improves performance in various environment settings, ensuring the safety of embodied AI systems. Notably, In complex environments with mixed obstacles, our method demonstrates a significant performance increase of 267\% compared to the baseline in attack scenarios, highlighting its robustness in challenging conditions.

翻译：具身智能系统，包括能够自主与物理世界交互的AI驱动机器人，有望通过大型语言模型（LLMs）得到显著推进。LLMs使机器人能够更好地理解复杂的语言指令，并以更强的理解力和适应性执行高级任务，这凸显了其提升具身AI能力的潜力。然而，这一进步也带来了安全挑战，尤其是在机器人导航任务中。不当的安全管理可能导致在复杂环境中的失败，并使系统易受恶意指令注入攻击，从而产生绕行或碰撞等不安全行为。为解决这些问题，我们提出了 \textit{SafeEmbodAI}，一个用于将移动机器人集成到具身智能系统中的安全框架。\textit{SafeEmbodAI} 融合了安全提示、状态管理和安全验证机制，以保护和协助LLMs通过多模态数据进行推理并验证其响应。我们设计了一种评估任务导向探索的指标，在模拟环境中的评估表明，我们的框架能有效缓解来自恶意命令的威胁，并在各种环境设置中提升性能，从而确保具身智能系统的安全性。值得注意的是，在具有混合障碍物的复杂环境中，我们的方法在攻击场景下相比基线实现了267%的显著性能提升，突显了其在挑战性条件下的鲁棒性。