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

2026-09-11

AI Summary

研究探索了利用零样本大型语言模型代理执行长期物理任务的可行性，通过设计一个集成了规划、工具调用、观察和验证功能的多代理框架，在农业任务中表现出色，并能更好地适应环境变化。

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. 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.

Problem

• Self-Adaptive Physical AI
• Long-Horizon Physical Tasks
• Zero-Shot Manner
• Environmental Changes
• Autonomous Management

Innovation

• Self-Adaptive Physical AI
• Zero-Shot Adaptation
• Multi-Agent Framework