Towards Learning to Perceive and Reason About Liquids
This work addresses the challenge of enabling robots to handle liquids, which is an incremental step as it applies existing deep learning methods to a new domain.
The paper tackled the problem of liquid perception and reasoning in robotics by applying fully-convolutional deep neural networks for detection and tracking, finding that LSTM networks outperformed single-frame and multi-frame models in these tasks.
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.