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dc.contributor.authorKloss, Alina
dc.contributor.authorBauza Villalonga, Maria
dc.contributor.authorWu, Jiajun
dc.contributor.authorTenenbaum, Joshua B
dc.contributor.authorRodriguez Garcia, Alberto
dc.contributor.authorBohg, Jeannette
dc.date.accessioned2021-12-08T12:41:23Z
dc.date.available2021-12-07T15:57:25Z
dc.date.available2021-12-07T18:30:17Z
dc.date.available2021-12-08T12:41:23Z
dc.date.issued2020-04
dc.identifier.issn1050-4729
dc.identifier.urihttps://hdl.handle.net/1721.1/138353.3
dc.description.abstract© 2020 IEEE. Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and, in environments that are only partially observable, executing contact-based tasks often suffers from low accuracy. We present an approach that addresses these two challenges for the problem of vision-based manipulation. First, we propose to disentangle contact from motion optimization. Thereby, we improve planning efficiency by focusing computation on promising contact locations. Second, we use a hybrid approach for perception and state estimation that combines neural networks with a physically meaningful state representation. In simulation and real-world experiments on the task of planar pushing, we show that our method is more efficient and achieves a higher manipulation accuracy than previous vision-based approaches.en_US
dc.description.sponsorshipMax Planck Societyen_US
dc.description.sponsorshipToyota Research Instituteen_US
dc.description.sponsorshipUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-16-1-2007)en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/ICRA40945.2020.9197409en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleAccurate Vision-based Manipulation through Contact Reasoningen_US
dc.typeArticleen_US
dc.identifier.citationKloss, Alina, Bauza, Maria, Wu, Jiajun, Tenenbaum, Joshua B, Rodriguez, Alberto et al. 2020. "Accurate Vision-based Manipulation through Contact Reasoning." Proceedings - IEEE International Conference on Robotics and Automation.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMIT-IBM Watson AI Lab
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.relation.journalProceedings - IEEE International Conference on Robotics and Automationen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-12-07T15:29:26Z
dspace.orderedauthorsKloss, A; Bauza, M; Wu, J; Tenenbaum, JB; Rodriguez, A; Bohg, Jen_US
dspace.date.submission2021-12-07T15:29:27Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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