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Bridging Intelligence and Instinct: A New Control Paradigm for Autonomous Robots

Shimian Zhang, Qiuhong Lu

TL;DR

The paper addresses the challenge of integrating powerful AI agents with real-world robotics while mitigating hallucinations and safety risks. It proposes a four-layer hierarchical architecture—External, Decision, Instinct, and Device—that decouples high-level deliberation from low-level safety and execution, using bidirectional feedback and instinctual refusals to enhance reliability. The Approach leverages multiple LLM-based agents in the Decision Layer, supported by a dedicated Instinct Layer that enforces safety, enabling robust operation even under AI faults. A Mobile Robot case study illustrates how the four-layer framework improves safety, scalability, and adaptability across diverse environments, signaling a path toward safer, more autonomous robotic systems capable of operating with limited human intervention.

Abstract

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.

Bridging Intelligence and Instinct: A New Control Paradigm for Autonomous Robots

TL;DR

The paper addresses the challenge of integrating powerful AI agents with real-world robotics while mitigating hallucinations and safety risks. It proposes a four-layer hierarchical architecture—External, Decision, Instinct, and Device—that decouples high-level deliberation from low-level safety and execution, using bidirectional feedback and instinctual refusals to enhance reliability. The Approach leverages multiple LLM-based agents in the Decision Layer, supported by a dedicated Instinct Layer that enforces safety, enabling robust operation even under AI faults. A Mobile Robot case study illustrates how the four-layer framework improves safety, scalability, and adaptability across diverse environments, signaling a path toward safer, more autonomous robotic systems capable of operating with limited human intervention.

Abstract

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.
Paper Structure (32 sections, 3 figures, 2 algorithms)

This paper contains 32 sections, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. AI Agents for Robotics
  4. LLMs as AI Agents
  5. AI Agents as the Robot Brain
  6. Robot System Architectures
  7. Safety Mechanisms
  8. Model-Based Safety Mechanisms
  9. Model-Free Safety Control
  10. AI Agents Safety Concerns
  11. Proposed Framework
  12. Layered Hierarchy Design
  13. External Layer
  14. Decision Layer
  15. Instinct Layer
  16. ...and 17 more sections

Figures (3)

  • Figure 1: Layered Hierarchy Design. The pyramid structure of our proposed architecture, comprising four layers: External, Decision, Instinct, and Device. The External layer represents high-level entities interacting with the system, the Decision layer acts as the 'brain' making high-level decisions, the Instinct layer continuously maintains safety acting as the 'brainstem,' and the Device layer executes the commands by controlling the robot's physical actions.
  • Figure 2: Modules and Flows. Drawing from our layered hierarchy, we can distill our framework into a few key modules: the (Artificial) General Intelligence module, which includes Human and/or Other Agents, the AI Agent module, the Robot Instinct module, and the Device Layer modules. The Device Layer is further subdivided into the Sensor, Executor, and Memory modules. This modular breakdown allows for targeted interactions and efficient data and control flows within the robotic system.
  • Figure 3: Feedback Mechanism.