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Conference Paper: Analytic approach for natural language based supervisory control of robotic manipulations

TitleAnalytic approach for natural language based supervisory control of robotic manipulations
Authors
Issue Date2016
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856
Citation
2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), Qingdao, China, 3-7 December 2016, p. 331-336 How to Cite?
AbstractRobots have been widely used in industrial and domestic areas. To control their behaviors, multiple approaches have been proposed to reduce the complexity of controlling robots. Among them, natural language (NL) control is attracting increasingly more attention due to its convenience and friendliness for the lay users. Existed approaches of natural language control focus on translating linguistic input into implementable action plans, while less attention were put on model check and property analysis, which are valued important and necessary for practical applications. To provide partial remedies to the problem, we propose to use State Transition Matrix (STM) to model the system behavior at task level. The matrix can be used to analyze the system properties from the control perspective, which provides reference for system design. In addition, STM supports to learn new skill in a hierarchical way with one-shot online interactive training. In this paper, we introduce the STM framework, describe how to analyze system property with STM, elaborate the learning algorithm, and illustrate the utility of this approach with experimental results.
Persistent Identifierhttp://hdl.handle.net/10722/262549

 

DC FieldValueLanguage
dc.contributor.authorCheng, Y-
dc.contributor.authorBao, J-
dc.contributor.authorJia, Y-
dc.contributor.authorDeng, Z-
dc.contributor.authorDong, L-
dc.contributor.authorXi, N-
dc.date.accessioned2018-09-28T05:01:13Z-
dc.date.available2018-09-28T05:01:13Z-
dc.date.issued2016-
dc.identifier.citation2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), Qingdao, China, 3-7 December 2016, p. 331-336-
dc.identifier.urihttp://hdl.handle.net/10722/262549-
dc.description.abstractRobots have been widely used in industrial and domestic areas. To control their behaviors, multiple approaches have been proposed to reduce the complexity of controlling robots. Among them, natural language (NL) control is attracting increasingly more attention due to its convenience and friendliness for the lay users. Existed approaches of natural language control focus on translating linguistic input into implementable action plans, while less attention were put on model check and property analysis, which are valued important and necessary for practical applications. To provide partial remedies to the problem, we propose to use State Transition Matrix (STM) to model the system behavior at task level. The matrix can be used to analyze the system properties from the control perspective, which provides reference for system design. In addition, STM supports to learn new skill in a hierarchical way with one-shot online interactive training. In this paper, we introduce the STM framework, describe how to analyze system property with STM, elaborate the learning algorithm, and illustrate the utility of this approach with experimental results.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856-
dc.relation.ispartofIEEE International Conference on Robotics and Biomimetics Proceedings-
dc.rightsIEEE International Conference on Robotics and Biomimetics Proceedings. Copyright © IEEE.-
dc.rights©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleAnalytic approach for natural language based supervisory control of robotic manipulations-
dc.typeConference_Paper-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.identifier.doi10.1109/ROBIO.2016.7866344-
dc.identifier.hkuros292805-
dc.identifier.spage331-
dc.identifier.epage336-
dc.publisher.placeUnited States-

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