RoboOS：一种面向跨具身与多智能体协作的分层具身框架
RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration
- State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机学院，北京大学）
- Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
RoboOS是首个基于“大脑-小脑”分层架构的开源具身系统，通过具身大脑模型、小脑技能库和实时共享内存三大组件，实现跨具身适应与多智能体高效协作。
AI中文摘要：
具身智能的曙光催生了对具备韧性、认知能力的多智能体协作的前所未有的迫切需求，这种协作贯穿于下一代生态系统，正在革新自主制造、自适应服务机器人和信息物理生产架构的范式。然而，当前的机器人系统面临显著局限，例如跨具身适应性有限、任务调度效率低下以及动态纠错能力不足。端到端VLA模型在长时程规划和任务泛化方面表现不足，而分层VLA模型则缺乏跨具身和多智能体协调能力。为应对这些挑战，我们提出了RoboOS，这是首个基于“大脑-小脑”分层架构构建的开源具身系统，实现了从单智能体到多智能体智能的范式转变。具体而言，RoboOS包含三个关键组件：（1）具身大脑模型（RoboBrain），一个用于全局感知和高层决策的多模态大语言模型（MLLM）；（2）小脑技能库，一个模块化、即插即用的工具集，支持多种技能的无缝执行；（3）实时共享内存，一种用于协调多智能体状态的时空同步机制。通过整合分层信息流，RoboOS将具身大脑与小脑技能库连接起来，促进长时程任务的稳健规划、调度和纠错，同时通过实时共享内存确保高效的多智能体协作。此外，我们增强了边云通信和基于云的分布式推理，以支持高频交互并实现可扩展部署。跨多种场景的大量真实世界实验证明了RoboOS在支持异构具身方面的通用性。项目网站：https://github.com/FlagOpen/RoboOS
英文摘要：
The dawn of embodied intelligence has ushered in an unprecedented imperative for resilient, cognition-enabled multi-agent collaboration across next-generation ecosystems, revolutionizing paradigms in autonomous manufacturing, adaptive service robotics, and cyber-physical production architectures. However, current robotic systems face significant limitations, such as limited cross-embodiment adaptability, inefficient task scheduling, and insufficient dynamic error correction. While End-to-end VLA models demonstrate inadequate long-horizon planning and task generalization, hierarchical VLA models suffer from a lack of cross-embodiment and multi-agent coordination capabilities. To address these challenges, we introduce RoboOS, the first open-source embodied system built on a Brain-Cerebellum hierarchical architecture, enabling a paradigm shift from single-agent to multi-agent intelligence. Specifically, RoboOS consists of three key components: (1) Embodied Brain Model (RoboBrain), a MLLM designed for global perception and high-level decision-making; (2) Cerebellum Skill Library, a modular, plug-and-play toolkit that facilitates seamless execution of multiple skills; and (3) Real-Time Shared Memory, a spatiotemporal synchronization mechanism for coordinating multi-agent states. By integrating hierarchical information flow, RoboOS bridges Embodied Brain and Cerebellum Skill Library, facilitating robust planning, scheduling, and error correction for long-horizon tasks, while ensuring efficient multi-agent collaboration through Real-Time Shared Memory. Furthermore, we enhance edge-cloud communication and cloud-based distributed inference to facilitate high-frequency interactions and enable scalable deployment. Extensive real-world experiments across various scenarios, demonstrate RoboOS's versatility in supporting heterogeneous embodiments. Project website: https://github.com/FlagOpen/RoboOS
