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dc.contributor.authorUd Din, Muhayy
dc.contributor.authorSarwar, M Usman
dc.contributor.authorZahoor, Imran
dc.contributor.authorQazi, Wajahat Mahmood
dc.contributor.authorRosell Gratacòs, Jan
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-09-25T11:17:37Z
dc.date.issued2019
dc.identifier.citationUd Din, M. [et al.]. Learning Action-oriented grasping for manipulation. A: IEEE International Conference on Emerging Technologies and Factory Automation. "Proceedings 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1575-1578.
dc.identifier.isbn978-1-7281-0302-0
dc.identifier.otherhttps://ioc.upc.edu/ca/personal/jan.rosell/publications/papers/2019_etfa_learning_action-oriented_grasping_for_manipulation_muhayyudinetal.pdf/view
dc.identifier.urihttp://hdl.handle.net/2117/168677
dc.description.abstractComplex manipulation tasks require grasping strategies that simultaneously satisfy the stability and the semantic constraints that have to be satisfied for an action to be feasible, referred as action-oriented semantic grasp strategies. This study develops a framework using machine learning techniques to compute action-oriented semantic grasps. It takes a 3D model of the object and the action to be performed as input and provides a vector of action-oriented semantic grasps. We evaluate the performance of machine learning (particu- larly classification techniques) to determine which approaches perform better for this problem. Using the best approaches, a multi-model classification technique is developed. The proposed approach is evaluated in simulation to grasp different kitchenobjects using a parallel gripper. The results show that multi-model classification approach enhances the prediction accuracy. The implemented system can be used as to automate the data labeling process required for deep learning approaches.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.lcshRobotics
dc.subject.lcshManipulators (Mecanism)
dc.titleLearning Action-oriented grasping for manipulation
dc.typeConference lecture
dc.subject.lemacRobòtica
dc.subject.lemacManipuladors (Mecanismes)
dc.contributor.groupUniversitat Politècnica de Catalunya. SIR - Service and Industrial Robotics
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac25834569
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/DPI2016-80077-R
dc.date.lift10000-01-01
local.citation.authorUd Din, M.; Sarwar, M. Usman; Zahoor, I.; Qazi, W.; Rosell, J.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.publicationNameProceedings 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation
local.citation.startingPage1575
local.citation.endingPage1578


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