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：随着通用人工智能（AGI）以惊人速度发展，大型语言模型（LLMs）作为AI代理在机器人领域的应用仍处于起步阶段。阻碍这些AI代理与机器人无缝集成的一个关键问题，是其生成内容的不可预测性——即所谓的"幻觉"现象。受生物神经系统启发，我们提出了一种新颖的分层式自主机器人架构，将AI代理智能与机器人本能相融合。在此语境下，我们将"机器人本能"定义为自主机器人系统中固有或习得的反应与优先级集合，这些机制确保安全保证、障碍规避等生存关键任务能够及时有效地执行。该范式和谐地结合了LLMs的智能与机器人行为的本能特性，为构建更安全、更具适应性的自主机器人系统做出贡献。通过移动机器人案例研究，我们展示了该范式显著提升自主机器人系统的潜力，并展望了未来机器人在多样化环境中实现独立安全运行的愿景。