Building Cooperative Embodied Agents Modularly with Large Language
  Models
Authors: name: Hongxin Zhang, name: Weihua Du, name: Jiaming Shan, name: Qinhong Zhou, name: Yilun Du, name: Joshua B. Tenenbaum, name: Tianmin Shu, name: Chuang Gan

Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we present a novel framework that utilizes LLMs for multi-agent cooperation and
tests it in various embodied environments. Our framework enables embodied
agents to plan, communicate, and cooperate with other embodied agents or humans
to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
such as GPT-4, can surpass strong planning-based methods and exhibit emergent
effective communication using our framework without requiring fine-tuning or
few-shot prompting. We also discover that LLM-based agents that communicate in
natural language can earn more trust and cooperate more effectively with
humans. Our research underscores the potential of LLMs for embodied AI and lays
the foundation for future research in multi-agent cooperation. Videos can be
found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/.