液体の知覚と推論に向けて
Towards Learning to Perceive and Reason About Liquids
深層学習で液体の検出と追跡に取り組み、単一フレーム・複数フレーム・LSTMの3モデルを比較した結果、LSTMが最も優れた性能を示した。
著者: Connor Schenck, Dieter Fox
分類: cs.RO, cs.CV
原文アブストラクト
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.
