UniLabOS: An AI-Native Operating System for Autonomous Laboratories

2025-12-25

AI Summary

Autonomous laboratory software systems suffer from a disconnect between high-level planning and low-level execution, alongside fragmented architectural designs, hindering scalable deployment. To address this, we propose the first AI-native operating system for autonomous laboratories. Our approach introduces three foundational innovations: (1) the A/R/A&R abstraction model for unified decision-execution modeling; (2) a dual-topology structural representation of laboratory infrastructure; and (3) the transactional CRUTD protocol for reliable, atomic experiment orchestration. The system employs typed, stateful abstractions, an edge-cloud distributed architecture, and decentralized service discovery—enabling protocol migration and human-AI co-governance. We validate the system across four real-world scenarios: liquid handling, organic synthesis, electrolyte formulation, and compute-intensive closed-loop experimentation. Results demonstrate robust heterogeneous instrument orchestration and seamless multi-node coordination, establishing a scalable foundation for next-generation autonomous laboratories.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Unifies fragmented software for autonomous labs
• Bridges digital planning with physical execution
• Enables scalable, reproducible autonomous experimentation

Innovation

• AI-native OS bridges digital planning with robotic execution
• Unified A/R/A&R model and dual-topology represent laboratory structure
• Transactional CRUTD protocol reconciles digital state with physical motion