  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.
