  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.
