Self-Building Labs 论文提出自搭建实验室（SBL）可根据科学目标自动设计并运行实验

Authors:Jiaru Bai, Yuchi Zhao, Han Hao, Elia Savino, Miroslav Bogdanovic, Derk H.W. ten Klooster, Simone Pilon, Timothy Noël, Yang Cao, Varinia Bernales, Alán Aspuru-Guzik

View PDF HTML (experimental)

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 requires 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 human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable. Project website: this https URL

Subjects:	Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Robotics (cs.RO)
Cite as:	arXiv:2610.23826 [cs.AI]
	(or arXiv:2610.23826v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.23826 arXiv-issued DOI via DataCite

Submission history

From: Jiaru Bai [view email]
[v1] Fri, 16 Oct 2026 17:48:29 UTC (14,503 KB)
[v2] Fri, 30 Oct 2026 17:54:41 UTC (17,800 KB)