文章基本信息

标题：Visual Intention Classification by Deep Learning for Gaze-based Human-Robot Interaction
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作者：Lei Shi ; Cosmin Copot ; Steve Vanlanduit 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：5
页码：750-755
DOI：10.1016/j.ifacol.2021.04.168
语种：English
出版社：Elsevier
摘要：AbstractIn this work, we propose a deep learning model to classify a human’s visual intention in gaze-based Human-Robot Interaction(HRI). We consider a scenario in which a human wears a pair of eye tracking glasses and can select an object by gaze and a robotic manipulator picks up the object. A neural network is trained as a binary classifier to classify if a human is looking at an object. The network architecture is based on Fully Convolutional Net(FCN), Convolutional Block Attention Modules(CBAM) and Residual Blocks. We evaluate our model with two experiments. In one experiment we test the performance in the scenario where only a single object exists and the other one multiple objects exist. The results show that our proposed network is accurate and it can generalize well. The F1 score on the single object is 0.971 and 0.962 on multiple objects.
关键词：Keywordsdeep learninggaze intentionHRI