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dc.contributor.authorYan, Fujian
dc.contributor.authorNannapaneni, Saideep
dc.contributor.authorHe, Hongsheng
dc.date.accessioned2020-02-22T22:46:30Z
dc.date.available2020-02-22T22:46:30Z
dc.date.issued2020-01-20
dc.identifier.citationF. Yan, S. Nannapaneni and H. He, "Robotic Scene Understanding by Using a Dictionary," 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, 2019, pp. 895-900en_US
dc.identifier.isbn978-172816321-5
dc.identifier.urihttps://doi.org/10.1109/ROBIO49542.2019.8961839
dc.identifier.urihttp://hdl.handle.net/10057/17098
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractScene understanding is a fundamental task for intelligent robots, especially in human-robot interaction. It is challenging due to the complexity of the human environment. In this paper, the proposed method integrates semantic analysis with object detection that enables robots to perceive scenes and deeply understands working environments. The model can extract deterministic entities of objects by analyzing their dictionary definitions. Therefore, robots can understand a scene at the object-level. These deterministic entities include the category, function, property, and composition of objects, and they can be used to generate feedback on how much robots can understand a scene by describing it in natural language. The feedback on how well robots understand the working space is an essential aspect to eliminate confusion during human-robot interactions. The experiment part of this paper discussed the applicability of the proposed method on robots.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2019 IEEE International Conference on Robotics and Biomimetics (ROBIO);2019
dc.subjectAutonomous robotsen_US
dc.subjectCognitive human-robot interactionen_US
dc.subjectRobot reasoningen_US
dc.subjectSemantic scene understandingen_US
dc.titleRobotic scene understanding by using a dictionaryen_US
dc.typeConference paperen_US
dc.rights.holder© 2019 IEEEen_US


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