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

arXiv: https://arxiv.org/abs/2404.16529 · HTML: https://mlsanity.com/paper/2404.16529/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Visual Closed-Loop Control for Pouring Liquids](https://mlsanity.com/paper/1610.02610/) (2016)
- [See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content](https://mlsanity.com/paper/1701.02718/) (2017)
- [Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization](https://mlsanity.com/paper/2011.10150/) (2020)
- [Accurate Pouring with an Autonomous Robot Using an RGB-D Camera](https://mlsanity.com/paper/1810.03303/) (2018)
- [Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications](https://mlsanity.com/paper/1404.7174/) (2014)
- [Tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach](https://mlsanity.com/paper/1602.00177/) (2016)
- [Chemistry Lab Automation via Constrained Task and Motion Planning](https://mlsanity.com/paper/2212.09672/) (2022)
- [Visual-Tactile Sensing for Real-time Liquid Volume Estimation in Grasping](https://mlsanity.com/paper/2202.11503/) (2022)
- [Designing the mobile robot Kevin for a life science laboratory](https://mlsanity.com/paper/2304.09090/) (2023)

## Built on by (most cited first, 2 of 2)

- [RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation](https://mlsanity.com/paper/2509.08820/) (2025)
- [Learning to Segment Liquids in Real-world Images](https://mlsanity.com/paper/2601.00940/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/DaniSchober/LabLiquidVision and the newly introduced dataset LabLiquidVolume is available at https://data.dtu.dk/articles/dataset/LabLiquidVision/25103102.