ロボット注ぎのための自己教師あり透明液体セグメンテーション
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
着色液体画像から透明液体画像を生成するモデルと背景差分を組み合わせ、手動アノテーションなしで透明液体のセグメンテーションを実現し、コップ内の液面高さを認識する注ぎタスクに応用した。
著者: Gautham Narayan Narasimhan, Kai Zhang, Ben Eisner, Xingyu Lin, David Held
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
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 images of colored liquids into synthetically generated transparent liquid images, trained only on an unpaired dataset of colored and transparent liquid images. Segmentation labels of colored liquids are obtained automatically using background subtraction. 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 the liquid height in a transparent cup. Accompanying video and supplementary materials can be found
