RoboOS构建分层机器人系统，实现多智能体跨体感协同与高效任务协作。
RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration
- 采用脑-小脑分层架构，分离高层决策与技能执行模块。
- 支持异构机器人在真实场景中完成长时序任务并动态纠错。
- 适合需要多机协作的智能制造、服务机器人等场景。
具身智能的兴起催生了下一代生态系统中具备认知能力的多智能体协同需求，革新了自主制造、自适应服务机器人及信息物理生产架构的范式。然而现有机器人系统存在跨体感适应性差、任务调度低效、动态纠错不足等问题。端到端视觉语言模型（VLA）在长时序规划与任务泛化上表现有限，而分层VLA模型缺乏跨体感与多智能体协调能力。为此，我们提出RoboOS，首个基于脑-小脑分层架构的开源具身系统，推动从单智能体向多智能体智能的范式转变。RoboOS包含三个核心组件：(1) 具身大脑模型（RoboBrain），一个用于全局感知与高层决策的多模态大语言模型；(2) 小脑技能库，一个模块化、即插即用的技能执行工具包；(3) 实时共享内存，一种时空同步机制，用于协调多智能体状态。通过整合分层信息流，RoboOS连接具身大脑与小脑技能库，实现鲁棒的长时序任务规划、调度与错误修正，并借助实时共享内存实现高效的多智能体协作。此外，我们优化边缘-云端通信与云端分布式推理，支持高频交互与可扩展部署。大量真实世界实验验证了RoboOS在多种场景下对异构体感的支持能力。
原文摘要 · Abstract (English)
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
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