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Article: 3-D Deformable Object Manipulation using Deep Neural Networks

Title3-D Deformable Object Manipulation using Deep Neural Networks
Authors
KeywordsStrain
Deformable models
Three-dimensional displays
Neural networks
Histograms
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE
Citation
IEEE Robotics and Automation Letters, 2019, v. 4 n. 4, p. 4255-4261 How to Cite?
AbstractDue to its high dimensionality, deformable object manipulation is a challenging problem in robotics. In this letter, we present a deep neural network based controller to servo control the position and shape of deformable objects with unknown deformation properties. In particular, a multi-layer neural network is used to map between the robotic end-effector's movement and the object's deformation measurement using an online learning strategy. In addition, we introduce a novel feature to describe deformable objects’ deformation used in visual servoing. This feature is directly extracted from the 3-D point cloud rather from the 2-D image as in previous work. In addition, we perform simultaneous tracking and reconstruction for the deformable object to resolve the partial observation problem during the deformable object manipulation. We validate the performance of our algorithm and controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo control for general deformable objects with a wide variety of goal settings. Experiment videos are available at https://sites.google.com/view/mso-deep.
Persistent Identifierhttp://hdl.handle.net/10722/273146
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHu, Z-
dc.contributor.authorHan, T-
dc.contributor.authorSun, P-
dc.contributor.authorPan, J-
dc.contributor.authorManocha, D-
dc.date.accessioned2019-08-06T09:23:23Z-
dc.date.available2019-08-06T09:23:23Z-
dc.date.issued2019-
dc.identifier.citationIEEE Robotics and Automation Letters, 2019, v. 4 n. 4, p. 4255-4261-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/273146-
dc.description.abstractDue to its high dimensionality, deformable object manipulation is a challenging problem in robotics. In this letter, we present a deep neural network based controller to servo control the position and shape of deformable objects with unknown deformation properties. In particular, a multi-layer neural network is used to map between the robotic end-effector's movement and the object's deformation measurement using an online learning strategy. In addition, we introduce a novel feature to describe deformable objects’ deformation used in visual servoing. This feature is directly extracted from the 3-D point cloud rather from the 2-D image as in previous work. In addition, we perform simultaneous tracking and reconstruction for the deformable object to resolve the partial observation problem during the deformable object manipulation. We validate the performance of our algorithm and controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo control for general deformable objects with a wide variety of goal settings. Experiment videos are available at https://sites.google.com/view/mso-deep.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at https://www.ieee.org/membership-catalog/productdetail/showProductDetailPage.html?product=PER481-ELE-
dc.relation.ispartofIEEE Robotics and Automation Letters-
dc.rightsIEEE Robotics and Automation Letters. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx 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.subjectStrain-
dc.subjectDeformable models-
dc.subjectThree-dimensional displays-
dc.subjectNeural networks-
dc.subjectHistograms-
dc.title3-D Deformable Object Manipulation using Deep Neural Networks-
dc.typeArticle-
dc.identifier.emailPan, J: jpan@cs.hku.hk-
dc.identifier.authorityPan, J=rp01984-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LRA.2019.2930476-
dc.identifier.hkuros300332-
dc.identifier.volume4-
dc.identifier.issue4-
dc.identifier.spage4255-
dc.identifier.epage4261-
dc.publisher.placeUnited States-

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