论文/arXiv 2610.23826·12 天前

自构建实验室

为缓解SDL适配新实验需大量人工工程的问题，提出能按高层目标自设计、配置并运行的SBL，并在不同机器人和量子点合成中验证。

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原文链接：Self-Building Labs（arXiv 2610.23826 · 发布日期：2026-10-16）

Self-Building Labs

为缓解SDL适配新实验需大量人工工程的问题，提出能按高层目标自设计、配置并运行的SBL，并在不同机器人和量子点合成中验证。

核心要点

• 现有SDL虽能自动执行与优化实验，但通常只能运行预先构建的流程，实验室灵活性成为瓶颈。
• SBL从空房间或已有SDL出发，补齐实验室布局、数字孪生、机器人技能与实验工作流。
• 系统在不同机器人和装置上运行，可从单工作站扩展到多机器人协作实验室。
• 通过真实世界量子点合成实验验证端到端能力，并提供项目网站。

验证范围与局限：端到端验证范围为真实世界量子点合成实验，并展示不同机器人与装置。

英文摘要 (Abstract)

Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still takes extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting the role of human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable. Project website: https://self-building-labs.github.io/

作者：Jiaru Bai, Yuchi Zhao, Han Hao, Miroslav Bogdanović, Yang Cao, Varinia Bernales, Alán Aspuru‐Guzik

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.23826：Self-Building Labs访问 2026-10-21