CAMON: Cooperative Agents for Multi-Object Navigation with LLM-based Conversations

TL;DR

This paper introduces CAMON, a decentralized multi-agent navigation framework that uses LLM-based communication to improve collaboration, efficiency, and robustness in complex household object navigation tasks.

Contribution

It presents a novel communication scheme and leadership organization structure enabling effective multi-agent collaboration with fewer communications.

Findings

Faster team consensus achieved with fewer communication instances.

Enhanced navigation effectiveness and exploration efficiency.

Robustness in multi-object navigation even with increased team size.

Abstract

Visual navigation tasks are critical for household service robots. As these tasks become increasingly complex, effective communication and collaboration among multiple robots become imperative to ensure successful completion. In recent years, large language models (LLMs) have exhibited remarkable comprehension and planning abilities in the context of embodied agents. However, their application in household scenarios, specifically in the use of multiple agents collaborating to complete complex navigation tasks through communication, remains unexplored. Therefore, this paper proposes a framework for decentralized multi-agent navigation, leveraging LLM-enabled communication and collaboration. By designing the communication-triggered dynamic leadership organization structure, we achieve faster team consensus with fewer communication instances, leading to better navigation effectiveness and collaborative exploration efficiency. With the proposed novel communication scheme, our framework promises to be conflict-free and robust in multi-object navigation tasks, even when there is a surge in team size.