title: Building Cooperative Embodied Agents Modularly with Large Language
    Models
summary: 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/.
paper_authors._id: 64a61b52ae2fee268a9295ee
paper_authors.name: Hongxin Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-06T14:43:05.429Z
paper_authors._id: 6319ef09bc8f3b313f7d04d1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1663253467220-6319ef09bc8f3b313f7d04d1.jpeg
paper_authors.fullname: Hongxin Zhang
paper_authors.type: user
paper_authors.user: Icefox
paper_authors._id: 64a61b52ae2fee268a9295ef
paper_authors.name: Weihua Du
paper_authors._id: 64a61b52ae2fee268a9295f0
paper_authors.name: Jiaming Shan
paper_authors._id: 64a61b52ae2fee268a9295f1
paper_authors.name: Qinhong Zhou
paper_authors._id: 64a61b52ae2fee268a9295f2
paper_authors.name: Yilun Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-06T13:07:21.899Z
paper_authors._id: 63c9bd445fdc575773c732fe
paper_authors.avatarUrl: /avatars/def472d1ab3fbf751225357c0932ae7e.svg
paper_authors.fullname: Yilun Du
paper_authors.type: user
paper_authors.user: yilundu
paper_authors._id: 64a61b52ae2fee268a9295f3
paper_authors.name: Joshua B. Tenenbaum
paper_authors._id: 64a61b52ae2fee268a9295f4
paper_authors.name: Tianmin Shu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-07T07:35:33.727Z
paper_authors._id: 618af79af281834c95311402
paper_authors.avatarUrl: /avatars/58ba086eb2309657d823b5768616eafe.svg
paper_authors.fullname: Tianmin Shu
paper_authors.type: user
paper_authors.user: tshu
paper_authors._id: 64a61b52ae2fee268a9295f5
paper_authors.name: Chuang Gan
paper_title: Building Cooperative Embodied Agents Modularly with Large Language
    Models
paper_summary: 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/.
paper_ai_summary: LLMs enable effective multi-agent cooperation and communication without fine-tuning or few-shot prompting, surpassing strong planning methods and enhancing human trust.
paper_ai_keywords: Large Language Models LLMs multi-agent cooperation embodied agents long-horizon tasks emergent communication trust embodied AI GPT-4