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Conference Paper: Feedback of robot states for object detection in natural language controlled robotic systems

TitleFeedback of robot states for object detection in natural language controlled robotic systems
Authors
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856
Citation
The 2015 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2015), Zhuhai, China, 6-9 December 2015. In Conference Proceedings, 2015, p. 875-880 How to Cite?
AbstractControlling robots with natural language enables untrained users to interact with them more easily. A significant challenge for such systems is the mismatched visual perceptual capabilities between humans and robots. Most existing methods try to improve the perceptual ability of robots by either developing robust vision algorithms to describe and identify objects more accurately, or refining the object segmentation through human collaboration. In this paper, we present a novel method to detect and track objects, and even discover previously undetected objects (e.g. objects occluded by or stacked on other objects) by incorporating feedback of robot states into the vision module. By reasoning about the object states according to the trajectories of robot states and then re-detecting the point clouds of the objects, the representation of the environment can be efficiently and accurately updated. Experimental results demonstrate the effectiveness and advantages of the proposed method. © 2015 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/235015
ISBN

 

DC FieldValueLanguage
dc.contributor.authorBao, J-
dc.contributor.authorJia, Y-
dc.contributor.authorCheng, Y-
dc.contributor.authorTang, H-
dc.contributor.authorXi, N-
dc.date.accessioned2016-10-14T13:50:44Z-
dc.date.available2016-10-14T13:50:44Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2015), Zhuhai, China, 6-9 December 2015. In Conference Proceedings, 2015, p. 875-880-
dc.identifier.isbn978-146739674-5-
dc.identifier.urihttp://hdl.handle.net/10722/235015-
dc.description.abstractControlling robots with natural language enables untrained users to interact with them more easily. A significant challenge for such systems is the mismatched visual perceptual capabilities between humans and robots. Most existing methods try to improve the perceptual ability of robots by either developing robust vision algorithms to describe and identify objects more accurately, or refining the object segmentation through human collaboration. In this paper, we present a novel method to detect and track objects, and even discover previously undetected objects (e.g. objects occluded by or stacked on other objects) by incorporating feedback of robot states into the vision module. By reasoning about the object states according to the trajectories of robot states and then re-detecting the point clouds of the objects, the representation of the environment can be efficiently and accurately updated. Experimental results demonstrate the effectiveness and advantages of the proposed method. © 2015 IEEE.-
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©2015 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.titleFeedback of robot states for object detection in natural language controlled robotic systems-
dc.typeConference_Paper-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ROBIO.2015.7418881-
dc.identifier.scopuseid_2-s2.0-84964434674-
dc.identifier.hkuros269347-
dc.identifier.spage875-
dc.identifier.epage880-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161019-

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