Vision-based robot manipulation of transparent liquid containers in a laboratory setting

TL;DR

This paper presents a cost-effective, vision-based system for estimating liquid volumes and automating pouring in laboratory settings, enabling robotic handling of transparent containers with small openings.

Contribution

Introduces a novel vision-based liquid volume estimation and simulation-driven pouring method tailored for small-opening containers in labs.

Findings

Effective volume estimation accuracy demonstrated

Successful integration with UR5 robotic arm for cell culture automation

System is fully reproducible with shared code and dataset

Abstract

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.