Transparent Liquid Segmentation for Robotic Pouring

Gautham Narayan Narasimhan, Kai Zhang, Ben Eisner, Xingyu Lin, David Held

Keywords: Generative Models, Transparent Liquid Segmentation, Pouring

TL;DR: We propose a novel segmentation pipeline that can segment transparent liquids such as water and demonstrate it's real world utility.

Abstract: Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem. We propose a novel segmentation pipeline that can segment transparent liquids such as water from a static, RGB image without requiring any manual annotations or heating of the liquid for training. Instead, we use a generative model that is capable of translating unpaired images of colored liquids into synthetically generated transparent liquid images. Segmentation labels of colored liquids are obtained automatically using background subtraction. We use paired samples of synthetically generated transparent liquid images and background subtraction for our segmentation pipeline. Our experiments show that we are able to accurately predict a segmentation mask for transparent liquids without requiring any manual annotations. We demonstrate the utility of transparent liquid segmentation in a robotic pouring task that controls pouring by perceiving liquid height in a transparent cup. Accompanying video and supplementary information can be found at https://sites.google.com/view/roboticliquidpouring