Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model - 文章详情页

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It　is　hoped　that　the　robot　could　interact　with　the　human　when　the　robots　help　us　in　our　daily　lives.　And　understanding　humans’　specific　intention　is　the　first　crucial　task　for　human-robot　interaction.　In　this　paper,　we　firstly　develop　a　multi-task　model　for　recognizing　humans’　intention,　which　is　composed　of　two　sub-tasks:　human　action　recognition　and　hand-held　object　identification.　For　the　front　subtask,　an　effective　ST-GCN-LSTM　model　is　proposed　by　fusing　the　Spatial　Temporal　Graph　Convolutional　Networks　and　Long　Short　Term　Memory　Networks.　And　for　the　second　subtask,　the　YOLO　v3　model　is　adopted　for　the　hand-held　object　identification.　Then,　we　build　a　framework　for　robot　interacting　with　the　human.　Finally,　these　proposed　models　and　the　interacting　framework　are　verified　on　several　datasets　and　the　testing　results　show　the　effectiveness　of　the　proposed　models　and　the　framework.　©　2020　Elsevier　B.V.

关键词：

Palmprint recognition Human robot interaction Convolutional neural networks Long short-term memory

作者机构：

[ 1 ] [Liu, Chunfang]Faculty of Information and Technology, Beijing University of Technology, Beijing, China
[ 2 ] [Li, Xiaoli]Faculty of Information and Technology, Beijing University of Technology, Beijing, China
[ 3 ] [Li, Qing]Faculty of Information and Technology, Beijing University of Technology, Beijing, China
[ 4 ] [Xue, Yaxin]Faculty of Information and Technology, Beijing University of Technology, Beijing, China
[ 5 ] [Liu, Huijun]Faculty of Information and Technology, Beijing University of Technology, Beijing, China
[ 6 ] [Gao, Yize]Faculty of Information and Technology, Beijing University of Technology, Beijing, China

通讯作者信息：

李晓理
[li, xiaoli]faculty of information and technology, beijing university of technology, beijing, china

电子邮件地址：

lixiaolibjut@bjut.edu.cn

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