LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution
Hongwei Cui, Yuyang Du, Qun Yang, Yulin Shao, Soung Chang Liew
TL;DR
The paper addresses the challenge of executing complex, intention-driven tasks in IoT environments by integrating Large Language Models with domain-specific AI modules and IoT devices. It introduces LLMind, a framework that uses a Language-FSM-Code pipeline to translate high-level user instructions into precise executable scripts, guided by a brain-inspired functional specialization concept and reinforced by an experience archive for rapid reuse. A central coordinator with context storage and script execution orchestrates interactions among modules and devices, enabling efficient, adaptable task completion on edge infrastructure. Demonstrations in check-in/security and network management validate the approach's feasibility and potential for scalable, user-friendly, multi-device coordination in intelligent IoT ecosystems.
Abstract
Task-oriented communications are an important element in future intelligent IoT systems. Existing IoT systems, however, are limited in their capacity to handle complex tasks, particularly in their interactions with humans to accomplish these tasks. In this paper, we present LLMind, an LLM-based task-oriented AI agent framework that enables effective collaboration among IoT devices, with humans communicating high-level verbal instructions, to perform complex tasks. Inspired by the functional specialization theory of the brain, our framework integrates an LLM with domain-specific AI modules, enhancing its capabilities. Complex tasks, which may involve collaborations of multiple domain-specific AI modules and IoT devices, are executed through a control script generated by the LLM using a Language-Code transformation approach, which first converts language descriptions to an intermediate finite-state machine (FSM) before final precise transformation to code. Furthermore, the framework incorporates a novel experience accumulation mechanism to enhance response speed and effectiveness, allowing the framework to evolve and become progressively sophisticated through continuing user and machine interactions.