# Towards Learning to Perceive and Reason About Liquids

arXiv: https://arxiv.org/abs/1608.00887 · HTML: https://mlsanity.com/paper/1608.00887/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Self-supervised Transparent Liquid Segmentation for Robotic Pouring](https://mlsanity.com/paper/2203.01538/) (2022)
- [The Sound of Water: Inferring Physical Properties from Pouring Liquids](https://mlsanity.com/paper/2411.11222/) (2024)

## Abstract (arXiv)

Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network. Our results show that the best liquid detection results are achieved when aggregating data over multiple frames and that the LSTM network outperforms the other two in both tasks. This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers.