Towards Learning to Perceive and Reason About Liquids
Towards Learning to Perceive and Reason About Liquids
复制标题
学习感知和推理液体
DOI：
10.1007/978-3-319-50115-4_43
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发表时间：
2016
影响因子：
5.4
通讯作者：
D. Fox
中科院分区：
文献类型：
--
作者：
Connor Schenck;D. Fox
人工智能和机器人学的最新进展声称，深度学习取得了许多令人难以置信的结果，但到目前为止，还没有人将深度学习应用于液体感知和推理问题。本文将全卷积深度神经网络应用于液体的检测和跟踪。我们评估了三种模型：单帧网络、多帧网络和LSTM递归网络。实验结果表明，当在多个帧上聚合数据时，液体检测效果最好，并且LSTM网络在这两个任务中的性能都优于其他两个网络。这表明，基于LSTM的神经网络有可能成为使机器人能够使用健壮的闭环系统控制器处理液体的关键组件。
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.