迈向具身智能体AI：基于LLM与VLM驱动的机器人自主与交互研究综述与分类
Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
AI总结：
本文针对LLM、VLM等基础模型驱动的具身机器人自主与交互领域开展综述，梳理相关研究与产业项目，提出模型集成分类体系并对比不同方案中智能体的角色。
摘要：
包括大语言模型（LLMs）和视觉语言模型（VLMs）在内的基础模型，近期为机器人自主能力与人机交互界面开辟了全新实现路径。与此同时，视觉-语言-动作模型（VLAs）或称大行为模型（LBMs），正不断提升机器人系统的灵巧度与任务能力。本综述梳理了推动智能体应用与架构发展的相关研究，涵盖早期基于GPT风格接口的探索，以及AI智能体承担协调器、规划器、感知执行器或通用交互接口的更复杂系统。这类智能体架构支持机器人基于自然语言指令推理、调用API、规划任务序列，或是辅助执行操作与故障诊断。考虑到该领域发展迭代速度极快，除同行评议研究成果外，本文还重点纳入了体现新兴趋势的社区驱动项目、ROS功能包与工业框架。本文提出了一套用于划分模型集成方案的分类体系，并对现有文献中不同解决方案里智能体承担的角色展开了对比分析。
英文摘要：
Foundation models, including large language models (LLMs) and vision-language models (VLMs), have recently enabled novel approaches to robot autonomy and human-robot interfaces. In parallel, vision-language-action models (VLAs) or large behavior models (LBMs) are increasing the dexterity and capabilities of robotic systems. This survey paper reviews works that advance agentic applications and architectures, including initial efforts with GPT-style interfaces and more complex systems where AI agents function as coordinators, planners, perception actors, or generalist interfaces. Such agentic architectures allow robots to reason over natural language instructions, invoke APIs, plan task sequences, or assist in operations and diagnostics. In addition to peer-reviewed research, due to the fast-evolving nature of the field, we highlight and include community-driven projects, ROS packages, and industrial frameworks that show emerging trends. We propose a taxonomy for classifying model integration approaches and present a comparative analysis of the role that agents play in different solutions in today's literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
