# Understanding Dynamic Tactile Sensing for Liquid Property Estimation

arXiv: https://arxiv.org/abs/2205.08771 · HTML: https://mlsanity.com/paper/2205.08771/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2022.

## Built on by (most cited first, 6 of 6)

- [Binding Touch to Everything: Learning Unified Multimodal Tactile Representations](https://mlsanity.com/paper/2401.18084/) (2024)
- [Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation](https://mlsanity.com/paper/2504.12908/) (2025)
- [Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction](https://mlsanity.com/paper/2308.02715/) (2023)
- [Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning](https://mlsanity.com/paper/2412.02119/) (2024)
- [Learning to Double Guess: An Active Perception Approach for Estimating the Center of Mass of Arbitrary Objects](https://mlsanity.com/paper/2502.02663/) (2025)
- [ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation](https://mlsanity.com/paper/2505.20498/) (2025)

## Abstract (arXiv)

Humans perceive the world by interacting with objects, which often happens in a dynamic way. For example, a human would shake a bottle to guess its content. However, it remains a challenge for robots to understand many dynamic signals during contact well. This paper investigates dynamic tactile sensing by tackling the task of estimating liquid properties. We propose a new way of thinking about dynamic tactile sensing: by building a light-weighted data-driven model based on the simplified physical principle. The liquid in a bottle will oscillate after a perturbation. We propose a simple physics-inspired model to explain this oscillation and use a high-resolution tactile sensor GelSight to sense it. Specifically, the viscosity and the height of the liquid determine the decay rate and frequency of the oscillation. We then train a Gaussian Process Regression model on a small amount of the real data to estimate the liquid properties. Experiments show that our model can classify three different liquids with 100% accuracy. The model can estimate volume with high precision and even estimate the concentration of sugar-water solution. It is data-efficient and can easily generalize to other liquids and bottles. Our work posed a physically-inspired understanding of the correlation between dynamic tactile signals and the dynamic performance of the liquid. Our approach creates a good balance between simplicity, accuracy, and generality. It will help robots to better perceive liquids in different environments such as kitchens, food factories, and pharmaceutical factories.