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