Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2025

Abstract

Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLMdriven reward functions to provide a more thoughtful and promising approach to cooperation. Another key feature of our method is the introduction of a plan evaluation module, which not only prevents agent action confusion caused by frequent plan updates but also ensures plan updates when the current plan becomes unsuitable. Experimental results show that the proposed method performs excellently in planning, communication, and collaboration on embodied environments (CWAH and TDW-MAT), efficiently completing long-term, complex tasks and significantly outperforming existing methods.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 2025 IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, June 11-15

First Page

29513

Last Page

29522

City or Country

Nashville, USA

Additional URL

https://openaccess.thecvf.com/content/CVPR2025/papers/Zu_Collaborative_Tree_Search_for_Enhancing_Embodied_Multi-Agent_Collaboration_CVPR_2025_paper.pdf

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