Show simple item record

dc.contributor.authorRao, Bharath
dc.contributor.authorKrishnan, Krishna K.
dc.contributor.authorHe, Hongsheng
dc.date.accessioned2019-03-21T20:03:04Z
dc.date.available2019-03-21T20:03:04Z
dc.date.issued2018-10
dc.identifier.citationA. B. Rao, K. Krishnan and H. He, "Learning Robotic Grasping Strategy Based on Natural-Language Object Descriptions," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, 2018, pp. 882-887en_US
dc.identifier.isbn978-1-5386-8094-0
dc.identifier.issn2153-0858
dc.identifier.otherWOS:000458872701004
dc.identifier.urihttps://doi.org/10.1109/IROS.2018.8593886
dc.identifier.urihttp://hdl.handle.net/10057/15970
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractGiven the description of an object's physical attributes, humans can determine a proper strategy and grasp an object. This paper proposes an approach to determine grasping strategy for an anthropomorphic robotic hand simply based on natural-language descriptions of an object. A learning-based approach is proposed to help a robotic hand learn suitable grasp poses starting from the natural language description of the object. Object features are parsed from natural-language descriptions by using a customized natural-language processing technique. The most likely grasp type for the given object is learned from the human grasping taxonomy based on the parsed features. The grasping strategy generated by the proposed approach is evaluated both by simulation study and execution of the grasps on an AR10 robotic hand.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);
dc.subjectRobotsen_US
dc.subjectGraspingen_US
dc.subjectTaxonomyen_US
dc.subjectShapeen_US
dc.subjectNatural language processingen_US
dc.subjectKinematicsen_US
dc.subjectTask analysisen_US
dc.titleLearning robotic grasping strategy based on natural-language object descriptionsen_US
dc.typeConference paperen_US
dc.rights.holder© 2018, IEEEen_US


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record