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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.21766 |
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| _version_ | 1866912789310734336 |
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| author | Gao, Jing Chang, Junhan Que, Haohui Xiong, Yanfei Zhang, Shixiang Qi, Xianwei Liu, Zhen Wang, Jun-Jie Ding, Qianjun Li, Xinyu Pan, Ziwei Xie, Qiming Yan, Zhuang Yan, Junchi Zhang, Linfeng |
| author_facet | Gao, Jing Chang, Junhan Que, Haohui Xiong, Yanfei Zhang, Shixiang Qi, Xianwei Liu, Zhen Wang, Jun-Jie Ding, Qianjun Li, Xinyu Pan, Ziwei Xie, Qiming Yan, Zhuang Yan, Junchi Zhang, Linfeng |
| contents | Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separates high-level planning from low-level execution. Here we present UniLabOS, an AI-native operating system for autonomous laboratories that bridges digital decision-making and embodied experimentation through typed, stateful abstractions and transactional safeguards. UniLabOS unifies laboratory elements via an Action/Resource/Action&Resource (A/R/A&R) model, represents laboratory structure with a dual-topology of logical ownership and physical connectivity, and reconciles digital state with material motion using a transactional CRUTD protocol. Built on a distributed edge-cloud architecture with decentralized discovery, UniLabOS enables protocol mobility across reconfigurable topologies while supporting human-in-the-loop governance. We demonstrate the system in four real-world settings -- a liquid-handling workstation, a modular organic synthesis platform, a distributed electrolyte foundry, and a decentralized computation-intensive closed-loop system -- showing robust orchestration across heterogeneous instruments and multi-node coordination. UniLabOS establishes a scalable foundation for agent-ready, reproducible, and provenance-aware autonomous experimentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21766 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniLabOS: An AI-Native Operating System for Autonomous Laboratories Gao, Jing Chang, Junhan Que, Haohui Xiong, Yanfei Zhang, Shixiang Qi, Xianwei Liu, Zhen Wang, Jun-Jie Ding, Qianjun Li, Xinyu Pan, Ziwei Xie, Qiming Yan, Zhuang Yan, Junchi Zhang, Linfeng Computational Engineering, Finance, and Science Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separates high-level planning from low-level execution. Here we present UniLabOS, an AI-native operating system for autonomous laboratories that bridges digital decision-making and embodied experimentation through typed, stateful abstractions and transactional safeguards. UniLabOS unifies laboratory elements via an Action/Resource/Action&Resource (A/R/A&R) model, represents laboratory structure with a dual-topology of logical ownership and physical connectivity, and reconciles digital state with material motion using a transactional CRUTD protocol. Built on a distributed edge-cloud architecture with decentralized discovery, UniLabOS enables protocol mobility across reconfigurable topologies while supporting human-in-the-loop governance. We demonstrate the system in four real-world settings -- a liquid-handling workstation, a modular organic synthesis platform, a distributed electrolyte foundry, and a decentralized computation-intensive closed-loop system -- showing robust orchestration across heterogeneous instruments and multi-node coordination. UniLabOS establishes a scalable foundation for agent-ready, reproducible, and provenance-aware autonomous experimentation. |
| title | UniLabOS: An AI-Native Operating System for Autonomous Laboratories |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2512.21766 |