标题： 面向具身代理人工智能：大模型和视觉语言模型驱动的机器人自主性和交互性的综述与分类 显示英文标题

标题： Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction

摘要： 基础模型，包括大型语言模型（LLMs）和视觉语言模型（VLMs），最近使得机器人自主性和人机接口有了新的方法。 同时，视觉语言动作模型（VLAs）或大型行为模型（LBMs）正在提高机器人系统的灵巧性和能力。 这篇综述论文关注那些朝着代理应用和架构发展的作品。 这包括最初的尝试探索GPT风格的工具接口，以及更复杂的系统，在这些系统中AI代理是协调者、规划者、感知执行者或通用接口。 这样的代理架构使机器人能够对自然语言指令进行推理，调用API，规划任务序列，或在操作和诊断中提供帮助。 除了同行评审的研究外，由于该领域的快速发展，我们还强调并包括展示新兴趋势的社区驱动项目、ROS包和工业框架。 我们提出了一种分类模型集成方法的分类法，并展示了代理在当今文献中不同解决方案中所扮演角色的比较分析。 显示英文摘要

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 focuses on those works advancing towards agentic applications and architectures. This includes initial efforts exploring GPT-style interfaces to tooling, as well as more complex system where AI agents are 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.