dc.contributor.author | Kloss, Alina | |
dc.contributor.author | Bauza Villalonga, Maria | |
dc.contributor.author | Wu, Jiajun | |
dc.contributor.author | Tenenbaum, Joshua B | |
dc.contributor.author | Rodriguez Garcia, Alberto | |
dc.contributor.author | Bohg, Jeannette | |
dc.date.accessioned | 2021-12-08T12:41:23Z | |
dc.date.available | 2021-12-07T15:57:25Z | |
dc.date.available | 2021-12-07T18:30:17Z | |
dc.date.available | 2021-12-08T12:41:23Z | |
dc.date.issued | 2020-04 | |
dc.identifier.issn | 1050-4729 | |
dc.identifier.uri | https://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.sponsorship | Max Planck Society | en_US |
dc.description.sponsorship | Toyota Research Institute | en_US |
dc.description.sponsorship | United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-16-1-2007) | en_US |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.isversionof | 10.1109/ICRA40945.2020.9197409 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | arXiv | en_US |
dc.title | Accurate Vision-based Manipulation through Contact Reasoning | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Kloss, 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.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.contributor.department | MIT-IBM Watson AI Lab | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | |
dc.relation.journal | Proceedings - IEEE International Conference on Robotics and Automation | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2021-12-07T15:29:26Z | |
dspace.orderedauthors | Kloss, A; Bauza, M; Wu, J; Tenenbaum, JB; Rodriguez, A; Bohg, J | en_US |
dspace.date.submission | 2021-12-07T15:29:27Z | |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Publication Information Needed | en_US |