# Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction

arXiv: https://arxiv.org/abs/2308.02715 · HTML: https://mlsanity.com/paper/2308.02715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction](https://mlsanity.com/paper/2211.14680/) (2022)
- [Enhancing Computational Fluid Dynamics with Machine Learning](https://mlsanity.com/paper/2110.02085/) (2021)
- [Segmenting Transparent Objects in the Wild](https://mlsanity.com/paper/2003.13948/) (2020)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [Interactive Perception: Leveraging Action in Perception and Perception in Action](https://mlsanity.com/paper/1604.03670/) (2016)
- [SPNets: Differentiable Fluid Dynamics for Deep Neural Networks](https://mlsanity.com/paper/1806.06094/) (2018)
- [FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation](https://mlsanity.com/paper/2303.02346/) (2023)
- [Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations](https://mlsanity.com/paper/2107.04556/) (2021)
- [Video Autoencoder: self-supervised disentanglement of static 3D structure and motion](https://mlsanity.com/paper/2110.02951/) (2021)
- [Visual Closed-Loop Control for Pouring Liquids](https://mlsanity.com/paper/1610.02610/) (2016)
- [A Modular Robotic Arm Control Stack for Research: Franka-Interface and FrankaPy](https://mlsanity.com/paper/2011.02398/) (2020)
- [Motion-based Object Segmentation based on Dense RGB-D Scene Flow](https://mlsanity.com/paper/1804.05195/) (2018)
- [Learning to Scaffold the Development of Robotic Manipulation Skills](https://mlsanity.com/paper/1911.00969/) (2019)
- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [ARChemist: Autonomous Robotic Chemistry System Architecture](https://mlsanity.com/paper/2204.13571/) (2022)
- [Understanding Dynamic Tactile Sensing for Liquid Property Estimation](https://mlsanity.com/paper/2205.08771/) (2022)
- [Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction](https://mlsanity.com/paper/2105.01456/) (2021)
- [Leveraging Multi-modal Sensing for Robotic Insertion Tasks in R&D Laboratories](https://mlsanity.com/paper/2307.00671/) (2023)

## Built on by (most cited first, 1 of 1)

- [ViscNet: Vision-Based In-line Viscometry for Fluid Mixing Process](https://mlsanity.com/paper/2512.01268/) (2025)

## Abstract (arXiv)

Accurately determining fluid viscosity is crucial for various industrial and scientific applications. Traditional methods of viscosity measurement, though reliable, often require manual intervention and cannot easily adapt to real-time monitoring. With advancements in machine learning and computer vision, this work explores the feasibility of predicting fluid viscosity by analyzing fluid oscillations captured in video data. The pipeline employs a 3D convolutional autoencoder pretrained in a self-supervised manner to extract and learn features from semantic segmentation masks of oscillating fluids. Then, the latent representations of the input data, produced from the pretrained autoencoder, is processed with a distinct inference head to infer either the fluid category (classification) or the fluid viscosity (regression) in a time-resolved manner. When the latent representations generated by the pretrained autoencoder are used for classification, the system achieves a 97.1% accuracy across a total of 4,140 test datapoints. Similarly, for regression tasks, employing an additional fully-connected network as a regression head allows the pipeline to achieve a mean absolute error of 0.258 over 4,416 test datapoints. This study represents an innovative contribution to both fluid characterization and the evolving landscape of Artificial Intelligence, demonstrating the potential of deep learning in achieving near real-time viscosity estimation and addressing practical challenges in fluid dynamics through the analysis of video data capturing oscillating fluid dynamics.