Dynamic Task Adaptation for Multi-Robot Manufacturing Systems with Large Language Models

TL;DR

This paper explores using large language models to enable real-time, adaptive task reassignment in multi-robot manufacturing systems, improving resilience and flexibility in dynamic environments.

Contribution

It introduces a novel LLM-enabled control framework for dynamic task reallocation, demonstrating its effectiveness in real-world experiments.

Findings

High task success rates in failure recovery

Effective interpretation of robot configuration data by LLM

Potential for enhanced adaptability in manufacturing

Abstract

Recent manufacturing systems are increasingly adopting multi-robot collaboration to handle complex and dynamic environments. While multi-agent architectures support decentralized coordination among robot agents, they often face challenges in enabling real-time adaptability for unexpected disruptions without predefined rules. Recent advances in large language models offer new opportunities for context-aware decision-making to enable adaptive responses to unexpected changes. This paper presents an initial exploratory implementation of a large language model-enabled control framework for dynamic task reassignment in multi-robot manufacturing systems. A central controller agent leverages the large language model's ability to interpret structured robot configuration data and generate valid reassignments in response to robot failures. Experiments in a real-world setup demonstrate high task success rates in recovering from failures, highlighting the potential of this approach to improve adaptability in multi-robot manufacturing systems.