Robotic understanding of scene contents and spatial constraints

dc.contributor.authorWilson, Dustin
dc.contributor.authorYan, Fujian
dc.contributor.authorSinha, Kaushik
dc.contributor.authorHe, Hongsheng
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThe aim of this paper is to create a model which is able to be used to accurately identify objects as well as spacial relationships in a dynamic environment. This paper proposed methods to train a deep learning model which recognizes unique objects and positions of key items in an environment. The model requires a low amount of images compared to others and also can recognize multiple objects in the same frame due to the utilization of region proposal networks. Methods are also discussed to find the position of recognized objects which can be used for picking up recognized items with a robotic arm. The system utilizes logic operations to be able to deduct how different objects relate to each other in regard to their placement from one another based off of the localization technique. The paper discusses how to create spacial relationships specifically.en_US
dc.identifier.citationWilson D., Yan F., Sinha K., He H. (2018) Robotic Understanding of Scene Contents and Spatial Constraints. In: Ge S. et al. (eds) Social Robotics. ICSR 2018. Lecture Notes in Computer Science, vol 11357. Springer, Chamen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofseriesICSR 2018 10th International Conference on Social Robotics;v.11357
dc.rights.holder© Springer Nature Switzerland AG 2018en_US
dc.subjectRobot spatial constraints reasoningen_US
dc.subjectSpatial logic understandingen_US
dc.subjectObject detectionen_US
dc.subjectConvolutional neural networksen_US
dc.subjectRobotic graspingen_US
dc.subjectFaster R-CNNen_US
dc.titleRobotic understanding of scene contents and spatial constraintsen_US
dc.typeConference paperen_US
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