Understanding Dynamic Tactile Sensing for Liquid Property Estimation
Understanding Dynamic Tactile Sensing for Liquid Property Estimation
复制标题
了解液体特性估计的动态触觉传感
DOI：
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复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wenzhen Yuan
中科院分区：
文献类型：
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作者：
Hung;Xiaofeng Guo;Wenzhen Yuan
人类通过与物体的互动来感知世界，这通常以动态的方式发生。例如，人类会摇动瓶子来猜测其内容。然而，对于机器人来说，如何很好地理解接触过程中的许多动态信号仍然是一个挑战。本文通过解决估计液体特性的任务来研究动态触觉传感。我们提出了一种思考动态触觉传感的新方法：基于简化的物理原理构建轻量级数据驱动模型。瓶子里的液体在受到扰动后会发生振荡。我们提出了一个简单的物理启发模型来解释这种振荡，并使用高分辨率触觉传感器 GelSight 来感知它。具体来说，液体的粘度和高度决定了振荡的衰减率和频率。然后，我们用少量的真实数据训练高斯过程回归模型来估计液体特性。实验表明，我们的模型可以以 100% 的准确率对三种不同的液体进行分类。该模型可以高精度估计体积，甚至可以估计糖水溶液的浓度。它具有数据高效性，并且可以轻松推广到其他液体和瓶子。我们的工作对动态触觉信号与液体动态性能之间的相关性提出了物理启发的理解。我们的方法在简单性、准确性和通用性之间实现了良好的平衡。它将帮助机器人更好地感知厨房、食品工厂、制药工厂等不同环境中的液体。
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.
影响因子：
2.5
作者：
SRINIVASAN, MA;LAMOTTE, RH
通讯作者：
LAMOTTE, RH