As the advent of artificial general intelligence (AGI) progresses at a breathtaking pace, the application of large language models (LLMs) as AI Agents in robotics remains in its nascent stage. A significant concern that hampers the seamless integration of these AI Agents into robotics is the unpredictability of the content they generate, a phenomena known as ``hallucination''. Drawing inspiration from biological neural systems, we propose a novel, layered architecture for autonomous robotics, bridging AI agent intelligence and robot instinct. In this context, we define Robot Instinct as the innate or learned set of responses and priorities in an autonomous robotic system that ensures survival-essential tasks, such as safety assurance and obstacle avoidance, are carried out in a timely and effective manner. This paradigm harmoniously combines the intelligence of LLMs with the instinct of robotic behaviors, contributing to a more safe and versatile autonomous robotic system. As a case study, we illustrate this paradigm within the context of a mobile robot, demonstrating its potential to significantly enhance autonomous robotics and enabling a future where robots can operate independently and safely across diverse environments.

翻译：随着人工通用智能（AGI）以惊人速度发展，大语言模型（LLMs）在机器人领域作为AI Agent的应用仍处于初级阶段。阻碍这些AI Agent无缝集成至机器人系统的重大隐患在于其生成内容的不确定性，即所谓的"幻觉"现象。受生物神经系统启发，我们提出一种面向自主机器人的新型分层架构，将AI Agent智能与机器人本能相融合。在此框架中，我们将"机器人本能"定义为自主机器人系统中先天或习得的响应与优先级集合，确保安全防护、避障等生存核心任务得以及时高效执行。该范式将LLMs的智能与机器人行为本能和谐统一，助力构建更安全、更通用的自主机器人系统。通过移动机器人案例研究，我们阐释了该范式的应用潜力，证明其能够显著增强自主机器人技术，为机器人跨环境独立安全运行开辟新路径。