Large language model agents adapt physical tasks better than reinforcement learning
Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?
Artificial Intelligence
Summary
Managing complex physical tasks over a long time, like farming, is hard because environments keep changing and agents have to act and observe continuously. The authors studied if large language model (LLM) agents can do this without extra training and human help. They built a system where multiple LLM agents plan, use tools, observe, and check work. Their tests showed these agents work as well as reinforcement learning methods under known conditions and adapt better when things change unexpectedly.
What this means in practice
- •For agriculture automation teams: Manage and adapt long-term farming operations using AI agents that require no retraining when weather changes.
- •For industrial robotics teams: Use LLM-based agents to maintain continuous control and adjustment in changing physical environments without manual intervention.
Authors
Varun Kaushik, Yayun Tan, Xiaofan Yu
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.