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Article: Modeling robotic operations controlled by natural language

TitleModeling robotic operations controlled by natural language
Authors
Issue Date2017
PublisherSouth China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and Springer-Verlag GmbH Germany. The Journal's web site is located at http://www.springer.com/engineering/control/journal/11768
Citation
Control Theory and Technology, 2017, v. 15 n. 4, p. 258-266 How to Cite?
AbstractThere are multiple ways to control a robotic system. Most of them require the users to have prior knowledge about robots or get trained before using them. Natural language based control attracts increasing attention due to its versatility and less requirements for users. Since natural language instructions from users cannot be understood by the robots directly, the linguistic input has to be processed into a formal representation which captures the task specification and removes the ambiguity inherent in natural language. For most of existing natural language controlled robotic system, they assume the given language instructions are already in correct orders. However, it is very likely for untrained users to give commands in a mixed order based on their direct observation and intuitive thinking. Simply following the order of the commands can lead to failures of tasks. To provide a remedy for the problem, we propose a novel framework named dependency relation matrix (DRM) to model and organize the semantic information extracted from language input, in order to figure out an executable sequence of subtasks for later execution. In addition, the proposed approach projects abstract language input and detailed sensory information into the same space, and uses the difference between the goal specification and temporal status of the task under implementation to monitor the progress of task execution. In this paper, we describe the DRM framework in detail, and illustrate the utility of this approach with experiment result
Persistent Identifierhttp://hdl.handle.net/10722/252338
ISSN
2015 SCImago Journal Rankings: 0.250

 

DC FieldValueLanguage
dc.contributor.authorCheng, Y-
dc.contributor.authorBao, J-
dc.contributor.authorJia, Y-
dc.contributor.authorDeng, Z-
dc.contributor.authorSun, Z-
dc.contributor.authorBi, S-
dc.contributor.authorLI, C-
dc.contributor.authorXi, N-
dc.date.accessioned2018-04-17T08:26:35Z-
dc.date.available2018-04-17T08:26:35Z-
dc.date.issued2017-
dc.identifier.citationControl Theory and Technology, 2017, v. 15 n. 4, p. 258-266-
dc.identifier.issn2095-6983-
dc.identifier.urihttp://hdl.handle.net/10722/252338-
dc.description.abstractThere are multiple ways to control a robotic system. Most of them require the users to have prior knowledge about robots or get trained before using them. Natural language based control attracts increasing attention due to its versatility and less requirements for users. Since natural language instructions from users cannot be understood by the robots directly, the linguistic input has to be processed into a formal representation which captures the task specification and removes the ambiguity inherent in natural language. For most of existing natural language controlled robotic system, they assume the given language instructions are already in correct orders. However, it is very likely for untrained users to give commands in a mixed order based on their direct observation and intuitive thinking. Simply following the order of the commands can lead to failures of tasks. To provide a remedy for the problem, we propose a novel framework named dependency relation matrix (DRM) to model and organize the semantic information extracted from language input, in order to figure out an executable sequence of subtasks for later execution. In addition, the proposed approach projects abstract language input and detailed sensory information into the same space, and uses the difference between the goal specification and temporal status of the task under implementation to monitor the progress of task execution. In this paper, we describe the DRM framework in detail, and illustrate the utility of this approach with experiment result-
dc.languageeng-
dc.publisherSouth China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and Springer-Verlag GmbH Germany. The Journal's web site is located at http://www.springer.com/engineering/control/journal/11768-
dc.relation.ispartofControl Theory and Technology-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.titleModeling robotic operations controlled by natural language-
dc.typeArticle-
dc.identifier.emailSun, Z: sunzy@hku.hk-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.identifier.doi10.1007/s11768-017-7099-5-
dc.identifier.hkuros284876-
dc.identifier.volume15-
dc.identifier.issue4-
dc.identifier.spage258-
dc.identifier.epage266-
dc.publisher.placeChina-

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