Vision-based robot manipulation of transparent liquid containers in a laboratory setting
This addresses the problem of manual, costly lab processes for researchers, though it is incremental as it builds on existing robotics and vision methods.
The paper tackles automating laboratory liquid handling by developing a vision-based system for volume estimation and a simulation-driven pouring method for small containers, achieving a 95% success rate in cell culture automation with a UR5 robot.
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.