发表机构
The Harker School; University of California, Merced（哈克学校; 加州大学默塞德分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探索零样本LLM多智能体框架管理长期物理任务，在农业场景中与强化学习对比，证明其同等效果且更适应环境变化，迈向自适应物理AI。
AI 中文摘要
大语言模型（LLM）智能体为无需人工干预地自主管理长期物理任务提供了一条有前景的路径。然而，物理任务要求智能体持续观察环境、做出关键行动，并在环境变化时保持有效性。现有方法要么需要大量数据和重新训练，要么主要侧重于在虚拟世界中运行的智能体。在本工作中，我们探索构建一个自适应物理AI智能体的可行性，该智能体以零样本方式管理长期物理任务，并在无需人工干预的情况下适应环境变化。我们设计了一个集成规划、工具调用、观察和验证的多智能体框架，并在不同天气模式下针对强化学习（RL）智能体在农业任务上对其进行评估。我们的结果表明，零样本LLM智能体在相同天气模式下能够达到与RL智能体相当的管理效果，并且在环境偏移情况下比RL更有效地适应，这凸显了迈向自适应物理AI智能体的有前景路径。
英文摘要
Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks in a zero-shot manner and adapts to environmental changes without human intervention. We design a multi-agent framework that integrates planning, tool calling, observation, and verification, and evaluate it on agricultural tasks against reinforcement learning (RL) agents under different weather patterns. Our results show that zero-shot LLM agents can achieve comparable management outcomes to RL agents under the same weather pattern and adapt more effectively than RL when evaluated under a shifted environment, highlighting a promising path toward self-adaptive physical AI agents.
Comments13 pages, 5 Figures, 3 Tables
