Integrating Large Language Models in Robotics to Empower Autonomous Agents with Natural Language Comprehension Capabilities: A Comprehensive Review

摘要

The integration of Large Language Models (LLMs) into robotic systems marks a transformative shift in human robot interaction by enabling robots to comprehend and generate natural language. This review explores how cutting edge LLMs such as GPT 4, PaLM E, and Flamingo are being deployed to enhance the autonomy, adaptability, and interactivity of robots across various domains. We analyze the technical foundations of LLMs and their applications in robotics, including instruction following, semantic understanding, and dialog-based interaction. The review highlights practical implementations in autonomous vehicles, industrial automation, healthcare, and smart home environments, illustrating how LLMs support more flexible, context aware robotic behaviors. However, we also identify critical challenges, including grounding language to physical actions, real time processing limitations, and multimodal fusion. The paper concludes by discussing future directions and interdisciplinary research opportunities that could further empower autonomous systems with human like language understanding and reasoning capabilities. This synthesis aims to inform researchers, developers, and policymakers on the current landscape and future potential of language empowered robotics.