RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909640448540672 |
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| author | Tan, Huajie Hao, Xiaoshuai Chi, Cheng Lin, Minglan Lyu, Yaoxu Cao, Mingyu Liang, Dong Chen, Zhuo Lyu, Mengsi Peng, Cheng He, Chenrui Ao, Yulong Lin, Yonghua Wang, Pengwei Wang, Zhongyuan Zhang, Shanghang |
| author_facet | Tan, Huajie Hao, Xiaoshuai Chi, Cheng Lin, Minglan Lyu, Yaoxu Cao, Mingyu Liang, Dong Chen, Zhuo Lyu, Mengsi Peng, Cheng He, Chenrui Ao, Yulong Lin, Yonghua Wang, Pengwei Wang, Zhongyuan Zhang, Shanghang |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration Tan, Huajie Hao, Xiaoshuai Chi, Cheng Lin, Minglan Lyu, Yaoxu Cao, Mingyu Liang, Dong Chen, Zhuo Lyu, Mengsi Peng, Cheng He, Chenrui Ao, Yulong Lin, Yonghua Wang, Pengwei Wang, Zhongyuan Zhang, Shanghang Robotics 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 |
| title | RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.03673 |