Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?

迈向自适应物理人工智能：大语言模型智能体能否管理长周期物理任务？

Abstract: 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.

摘要： 大语言模型（LLM）智能体为在无需人工干预的情况下自主管理长周期物理任务提供了一条充满希望的路径。然而，物理任务要求智能体能够持续观察环境、采取有效行动，并在环境发生变化时保持效能。现有的方法要么需要大量数据和重新训练，要么主要集中于在虚拟世界中运行的智能体。

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.

在这项工作中，我们探讨了构建一种自适应物理人工智能智能体的可行性，该智能体能够以零样本（zero-shot）方式管理长周期物理任务，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了规划、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的农业任务中，将其与强化学习（RL）智能体进行了对比评估。

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.

研究结果表明，在相同的天气模式下，零样本 LLM 智能体可以达到与强化学习智能体相当的管理成果；而在环境发生变化时，其适应能力优于强化学习智能体。这凸显了通往自适应物理人工智能智能体的一条充满希望的路径。