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.

翻译：人类通过与物体互动来感知世界, 通常以动态的方式发生。 例如, 人类会摇动一个瓶子来猜测其内容。 但是, 机器人在接触良好时仍要理解许多动态信号。 本文通过处理液体特性估算任务来调查动态触动感测。 我们提出一种新的思考方式, 思考动态触摸感测: 根据简化物理原理建立一个轻量数据驱动模型。 瓶中液体在扰动后会滚动。 我们提出一个简单的物理启发模型来解释这种振动, 并使用高分辨率触动传感器 GelSight 来感知它。 具体地说, 液体的粘度和高度决定着液体特性的衰减率和频率。 我们然后用少量真实数据来构建一个高压进程回归模型, 来估计液体特性。 实验显示我们的模型可以将三种不同的液体分解, 100%的精确性能。 模型可以以高精确度来估计量, 甚至以高清晰度的触觉感测高清晰度的触觉感测量, 和高清晰度的食品工厂的精度, 。 数据可以成为我们一般的精确度和精确度的精确度, 。 的精确度, 。 它可以将其它的精确度和感测的液体的精确度, 。 。