Understanding Dynamic Tactile Sensing for Liquid Property Estimation

TL;DR

This paper introduces a physics-inspired, data-efficient tactile sensing method for robots to accurately estimate liquid properties like volume, viscosity, and concentration using dynamic signals captured by a high-resolution sensor.

Contribution

It presents a novel, simplified physical model combined with Gaussian Process Regression for dynamic tactile sensing of liquids, achieving high accuracy and generalization with minimal data.

Findings

Classifies three liquids with 100% accuracy

Precisely estimates liquid volume and concentration

Generalizes well to different liquids and bottles

Abstract

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.