The proposed framework introduces a layered hierarchy design for autonomous robotic control systems, comprising four distinct layers: External, Decision, Instinct, and Device.
The External Layer represents high-level entities such as humans and other intelligent agents that interact with the system. The Decision Layer acts as the 'brain' of the robot, utilizing advanced AI agents like LLMs for complex decision-making and task planning. The Instinct Layer serves as the 'brainstem,' continuously maintaining safety and survival-essential tasks through independent, uninterrupted safety mechanisms. The Device Layer executes the commands by controlling the robot's physical actions.
This architecture bridges the intelligence of AI agents with the instinctual safety of robotic behaviors, addressing the key challenge of unpredictability or 'hallucination' associated with LLMs. The Instinct Layer provides a robust safety net, ensuring the robot's fundamental safety and self-preservation even in the face of potential incorrect decisions from the AI agent.
The framework also incorporates a feedback mechanism, where the AI agent receives information from the Instinct Layer to enable self-reflection and optimization of subsequent commands. This closed-loop structure enhances the safety and adaptability of the robotic system.
The authors demonstrate the efficacy of this approach through a case study involving a mobile robot, showcasing the potential to significantly improve the autonomy, safety, and versatility of robotic systems across diverse environments.
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by Shimian Zhan... at arxiv.org 05-01-2024
https://arxiv.org/pdf/2307.10690.pdfDeeper Inquiries