Self-driving laboratories (SDLs), where artificial intelligence proposes subsequent experiments and robotic systems execute them, are rapidly becoming the vanguard of materials discovery. A critical bottleneck, however, lies in seamlessly bridging diverse AI algorithms tailored for specific exploration goals with the heterogeneous robotic hardware found across different laboratories. Here, we present NIMO, an open-source software platform designed to dissolve this barrier through three core paradigms: a modular AI-robot decoupling mediated via simple CSV file exchange, a discrete candidate-pool architecture that seamlessly absorbs domain knowledge, and a unified Python interface pre-loaded with twelve distinct AI algorithms. In this Perspective, we review the operational principles of each algorithm alongside six diverse SDL implementations driven by NIMO, covering electrolyte discovery, organic synthesis, thin-film exploration, fuel-cell process informatics, coffee-ring phase exploration, and legacy liquid-handling automation. One of these also demonstrates NIMO's seamless interoperability with the IvoryOS orchestration framework. To democratize autonomous science, we also introduce a no-code desktop application that enables intuitive, human-in-the-loop exploration for non-programmers. NIMO is freely available at https://github.com/NIMS-DA/nimo, offering a versatile, plug-and-play foundation to accelerate autonomous materials exploration across diverse experimental landscapes.

翻译：摘要：自驱动实验室（SDL）中，人工智能提出后续实验方案并由机器人系统执行，正迅速成为材料发现的前沿阵地。然而，一个关键瓶颈在于：如何将针对特定探索目标定制的多样化AI算法，与不同实验室中异构的机器人硬件无缝衔接。为此，我们提出开源软件平台NIMO，通过三大核心范式打破这一壁垒：一是基于简单CSV文件交换的模块化AI-机器人解耦机制；二是无缝吸收领域知识的离散候选池架构；三是预置十二种不同AI算法的统一Python接口。在本前瞻性综述中，我们回顾了每种算法的运行原理，以及由NIMO驱动的六个不同SDL实现案例——涵盖电解液发现、有机合成、薄膜探索、燃料电池过程信息学、咖啡环相探索及传统液体处理自动化。其中一项案例还展示了NIMO与IvoryOS编排框架的无缝互操作性。为普及自动化科学，我们同步推出无代码桌面应用，使非编程人员能够直观地在人机协同环中进行探索。NIMO已开源发布于https://github.com/NIMS-DA/nimo，为加速跨多样化实验场景的自主材料探索提供了灵活即用的基础平台。