RoboOS-NeXT: A Unified Memory-based Framework for Lifelong, Scalable, and Robust Multi-Robot Collaboration
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917050439434240 |
|---|---|
| author | Tan, Huajie Chi, Cheng Chen, Xiansheng Ji, Yuheng Zhao, Zhongxia Hao, Xiaoshuai Lyu, Yaoxu Cao, Mingyu Zhao, Junkai Lyu, Huaihai Zhou, Enshen Chen, Ning Fu, Yankai Peng, Cheng Guo, Wei Liang, Dong Chen, Zhuo Lyu, Mengsi He, Chenrui Ao, Yulong Lin, Yonghua Wang, Pengwei Wang, Zhongyuan Zhang, Shanghang |
| author_facet | Tan, Huajie Chi, Cheng Chen, Xiansheng Ji, Yuheng Zhao, Zhongxia Hao, Xiaoshuai Lyu, Yaoxu Cao, Mingyu Zhao, Junkai Lyu, Huaihai Zhou, Enshen Chen, Ning Fu, Yankai Peng, Cheng Guo, Wei Liang, Dong Chen, Zhuo Lyu, Mengsi He, Chenrui Ao, Yulong Lin, Yonghua Wang, Pengwei Wang, Zhongyuan Zhang, Shanghang |
| contents | The proliferation of collaborative robots across diverse tasks and embodiments presents a central challenge: achieving lifelong adaptability, scalable coordination, and robust scheduling in multi-agent systems. Existing approaches, from vision-language-action (VLA) models to hierarchical frameworks, fall short due to their reliance on limited or dividual-agent memory. This fundamentally constrains their ability to learn over long horizons, scale to heterogeneous teams, or recover from failures, highlighting the need for a unified memory representation. To address these limitations, we introduce RoboOS-NeXT, a unified memory-based framework for lifelong, scalable, and robust multi-robot collaboration. At the core of RoboOS-NeXT is the novel Spatio-Temporal-Embodiment Memory (STEM), which integrates spatial scene geometry, temporal event history, and embodiment profiles into a shared representation. This memory-centric design is integrated into a brain-cerebellum framework, where a high-level brain model performs global planning by retrieving and updating STEM, while low-level controllers execute actions locally. This closed loop between cognition, memory, and execution enables dynamic task allocation, fault-tolerant collaboration, and consistent state synchronization. We conduct extensive experiments spanning complex coordination tasks in restaurants, supermarkets, and households. Our results demonstrate that RoboOS-NeXT achieves superior performance across heterogeneous embodiments, validating its effectiveness in enabling lifelong, scalable, and robust multi-robot collaboration. Project website: https://flagopen.github.io/RoboOS/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoboOS-NeXT: A Unified Memory-based Framework for Lifelong, Scalable, and Robust Multi-Robot Collaboration Tan, Huajie Chi, Cheng Chen, Xiansheng Ji, Yuheng Zhao, Zhongxia Hao, Xiaoshuai Lyu, Yaoxu Cao, Mingyu Zhao, Junkai Lyu, Huaihai Zhou, Enshen Chen, Ning Fu, Yankai Peng, Cheng Guo, Wei Liang, Dong Chen, Zhuo Lyu, Mengsi He, Chenrui Ao, Yulong Lin, Yonghua Wang, Pengwei Wang, Zhongyuan Zhang, Shanghang Robotics The proliferation of collaborative robots across diverse tasks and embodiments presents a central challenge: achieving lifelong adaptability, scalable coordination, and robust scheduling in multi-agent systems. Existing approaches, from vision-language-action (VLA) models to hierarchical frameworks, fall short due to their reliance on limited or dividual-agent memory. This fundamentally constrains their ability to learn over long horizons, scale to heterogeneous teams, or recover from failures, highlighting the need for a unified memory representation. To address these limitations, we introduce RoboOS-NeXT, a unified memory-based framework for lifelong, scalable, and robust multi-robot collaboration. At the core of RoboOS-NeXT is the novel Spatio-Temporal-Embodiment Memory (STEM), which integrates spatial scene geometry, temporal event history, and embodiment profiles into a shared representation. This memory-centric design is integrated into a brain-cerebellum framework, where a high-level brain model performs global planning by retrieving and updating STEM, while low-level controllers execute actions locally. This closed loop between cognition, memory, and execution enables dynamic task allocation, fault-tolerant collaboration, and consistent state synchronization. We conduct extensive experiments spanning complex coordination tasks in restaurants, supermarkets, and households. Our results demonstrate that RoboOS-NeXT achieves superior performance across heterogeneous embodiments, validating its effectiveness in enabling lifelong, scalable, and robust multi-robot collaboration. Project website: https://flagopen.github.io/RoboOS/ |
| title | RoboOS-NeXT: A Unified Memory-based Framework for Lifelong, Scalable, and Robust Multi-Robot Collaboration |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.26536 |