Fluid Property Prediction Leveraging AI and Robotics
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Inferring liquid properties from vision is a challenging task due to the complex nature of fluids, both in behavior and detection. Nevertheless, the ability to infer their properties directly from visual information is highly valuable for autonomous fluid handling systems, as cameras are readily available. Moreover, predicting fluid properties purely from vision can accelerate the process of fluid characterization saving considerable time and effort in various experimental environments. In this work, we present a purely vision-based approach to estimate viscosity, leveraging the fact that the behavior of the fluid oscillations is directly related to the viscosity. Specifically, we utilize a 3D convolutional autoencoder to learn latent representations of different fluid-oscillating patterns present in videos. We leverage this latent representation to visually infer the category of fluid or the dynamics viscosity of fluid from video.
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