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Article: Cloth Manipulation Using Random-Forest-Based Imitation Learning

TitleCloth Manipulation Using Random-Forest-Based Imitation Learning
Authors
KeywordsRobots
Task analysis
Feature extraction
Strain
Three-dimensional displays
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. 2, p. 2086-2093 How to Cite?
AbstractWe present a novel approach for manipulating high-DOE deformable objects such as cloth. Our approach uses a random-forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random-forest is determined automatically based on the training data, which consists of visual features and control signals. The training data is constructed online using an imitation learning algorithm. We have evaluated our approach on different cloth manipulation benchmarks such as flattening, folding, and twisting. In all these tasks, we have observed convergent behavior for the random-forest. On convergence, the random-forest-based controller exhibits superior robustness to observation noise compared with other techniques such as convolutional neural networks and nearest neighbor searches. Videos and supplemental material are available at http://gamma.cs.unc.edu/ClothM/.
Persistent Identifierhttp://hdl.handle.net/10722/273149
ISSN
2019 Impact Factor: 3.608

 

DC FieldValueLanguage
dc.contributor.authorJia, B-
dc.contributor.authorPan, Z-
dc.contributor.authorHu, Z-
dc.contributor.authorPan, J-
dc.contributor.authorManocha, D-
dc.date.accessioned2019-08-06T09:23:26Z-
dc.date.available2019-08-06T09:23:26Z-
dc.date.issued2019-
dc.identifier.citationIEEE Robotics and Automation Letters, 2019, v. 4 n. 2, p. 2086-2093-
dc.identifier.issn2377-3766-
dc.identifier.urihttp://hdl.handle.net/10722/273149-
dc.description.abstractWe present a novel approach for manipulating high-DOE deformable objects such as cloth. Our approach uses a random-forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random-forest is determined automatically based on the training data, which consists of visual features and control signals. The training data is constructed online using an imitation learning algorithm. We have evaluated our approach on different cloth manipulation benchmarks such as flattening, folding, and twisting. In all these tasks, we have observed convergent behavior for the random-forest. On convergence, the random-forest-based controller exhibits superior robustness to observation noise compared with other techniques such as convolutional neural networks and nearest neighbor searches. Videos and supplemental material are available at http://gamma.cs.unc.edu/ClothM/.-
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.subjectRobots-
dc.subjectTask analysis-
dc.subjectFeature extraction-
dc.subjectStrain-
dc.subjectThree-dimensional displays-
dc.titleCloth Manipulation Using Random-Forest-Based Imitation Learning-
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.2019.2897370-
dc.identifier.scopuseid_2-s2.0-85062718308-
dc.identifier.hkuros300343-
dc.identifier.volume4-
dc.identifier.issue2-
dc.identifier.spage2086-
dc.identifier.epage2093-
dc.publisher.placeUnited States-

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