Self-Building Labs· 自建实验室
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
自建实验室提升科学发现效率，实现自动化设计与配置实验环境。
- 核心方法
- 提出自建实验室（SBL）系统，能够根据高层次科学目标自动设计实验室布局、构建数字孪生、开发机器人技能以及优化实验流程。
- 适合谁读
- 适合研究者、工程师及实验室管理人员阅读，了解最新自动化实验室技术。
- 要解决的问题
- 现有自驾车实验室（SDLs）只能执行为其设计的实验，转换到新实验需要大量手动工程，限制了科学发现的扩展性。
- 关键实验
- SBL系统已经在不同的机器人和设置中进行了演示，证明了其多场景适用性。
- 主要贡献
- SBL减少了实验室适应新实验所需的手动工程，提高了实验室的灵活性和科学发现的效率。
- 意义与局限
- 意义在于大幅提升实验室自动化水平，减少实验转换成本，加速科学研究；局限性可能在于初期成本较高，且对于非标准实验的需求满足程度仍有待验证。