Integration of Large Language Models with Proximal Policy Optimization for Autonomous Mobile Robot Control in Dynamic Environments

초록

Autonomous mobile robots (AMRs) must operate in dynamic, unstructured environments where traditional control and reinforcement learning (RL) face adaptability and reward design limitations. This study proposes a hybrid framework combining proximal policy optimization (PPO) with a large language model (LLM) as an adaptive reward designer. Using GPT-4o-mini, the LLM dynamically shapes rewards based on performance logs, improving exploration and stability. Experiments in complex indoor navigation show the LLM-PPO model reduces collisions by 38%, shortens completion time by 21%, and increases rewards by 8% over PPO. Results demonstrate LLM-RL integration enhances safety, efficiency, and consistency, offering a promising paradigm for AMR control.

키워드