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Service humanoid robotics: a novel interactive system based on bionic-companionship framework.

作者信息

Yang Jiaji, Chew Esyin, Liu Pengcheng

机构信息

Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Cardiff, United Kingdom.

The Department of Computer Science, University of York, York, United Kingdom.

出版信息

PeerJ Comput Sci. 2021 Aug 13;7:e674. doi: 10.7717/peerj-cs.674. eCollection 2021.

DOI:10.7717/peerj-cs.674
PMID:34458575
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8371998/
Abstract

At present, industrial robotics focuses more on motion control and vision, whereas humanoid service robotics (HSRs) are increasingly being investigated and researched in the field of speech interaction. The problem and quality of human-robot interaction (HRI) has become a widely debated topic in academia. Especially when HSRs are applied in the hospitality industry, some researchers believe that the current HRI model is not well adapted to the complex social environment. HSRs generally lack the ability to accurately recognize human intentions and understand social scenarios. This study proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen's (2001) companionship theory and neural image captioning (NIC) generation algorithm. By integrating image-to-natural interactivity generation and communicating with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. Compared to previous research a novel interactive system is developed based on the bionic-companionship framework. The humanoid service robot was integrated with the system to conduct preliminary tests. The results show that the interactive system based on the bionic-companionship framework can help the service humanoid robot to effectively respond to changes in the interactive environment, for example give different responses to the same character in different scenes.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/a3e5a6683375/peerj-cs-07-674-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/5f7b70f5f2d8/peerj-cs-07-674-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/ab0204b7bac4/peerj-cs-07-674-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/eed32c047d01/peerj-cs-07-674-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/6680602e82a3/peerj-cs-07-674-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/635b6a79ba5a/peerj-cs-07-674-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/30cfbcda1d91/peerj-cs-07-674-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/b82c0d322a78/peerj-cs-07-674-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/a3e5a6683375/peerj-cs-07-674-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/5f7b70f5f2d8/peerj-cs-07-674-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/ab0204b7bac4/peerj-cs-07-674-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/eed32c047d01/peerj-cs-07-674-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/6680602e82a3/peerj-cs-07-674-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/635b6a79ba5a/peerj-cs-07-674-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/30cfbcda1d91/peerj-cs-07-674-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/b82c0d322a78/peerj-cs-07-674-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85d3/8371998/a3e5a6683375/peerj-cs-07-674-g008.jpg

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Learning where to attend with deep architectures for image tracking.
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