Laboratory processes involving small volumes of solutions and active ingredients are often performed manually due to challenges in automation, such as high initial costs, semi-structured environments and protocol variability. In this work, we develop a flexible and cost-effective approach to address this gap by introducing a vision-based system for liquid volume estimation and a simulation-driven pouring method particularly designed for containers with small openings. We evaluate both components individually, followed by an applied real-world integration of cell culture automation using a UR5 robotic arm. Our work is fully reproducible: we share our code at at \url{https://github.com/DaniSchober/LabLiquidVision} and the newly introduced dataset LabLiquidVolume is available at https://data.dtu.dk/articles/dataset/LabLiquidVision/25103102.

翻译：实验室中涉及小体积溶液和活性成分的流程往往因自动化面临的挑战（如高初始成本、半结构化环境及操作方案的可变性）而需手动执行。本研究开发了一种灵活且经济高效的方法来应对这一难题，通过引入基于视觉的液体体积估计系统，以及专门针对小口径容器的仿真驱动倾倒方法。我们分别对这两个模块进行了评估，随后利用UR5机械臂在真实场景中实现了细胞培养自动化集成。本工作完全可复现：相关代码发布于\url{https://github.com/DaniSchober/LabLiquidVision}，新引入的LabLiquidVolume数据集可从https://data.dtu.dk/articles/dataset/LabLiquidVision/25103102 获取。