[论文] Self-Building Labs

论文概要

研究领域: ML 作者: Jiaru Bai, Yuchi Zhao, Han Hao, Miroslav Bogdanovic, Yang Cao, Varinia Bernales, Alán Aspuru-Guzik 发布时间: 2026-10-16 arXiv: 2610.23826

中文摘要

自动驾驶实验室（SDL）通过自动化实验执行和优化来加速科学发现。AI 智能体越来越能提出新实验，但现有 SDL 只能运行为其构建的特定流程。让它们适应新实验仍需要大量人工工程，实验室灵活性成为扩展发现的瓶颈。我们提出自构建实验室（SBL），给定高层次的科学目标，自行设计、配置并运行自己的实验室设置。SBL 从任何已有条件出发——从空房间到已运行的 SDL——构建缺失的能力：实验室布局、数字孪生、机器人技能和实验工作流。我们展示了该系统在不同机器人和设置上工作，从单工作站扩展到多机器人协作实验室。通过真实的量子点合成实验验证了 SBL 的端到端能力。通过将人类研究者的角色从手动配置和编程设备自动化转变为指定科学目标，SBL 使科学自动化更易获取、更灵活、更可扩展。

原文摘要

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, s...

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