| dc.contributor.author | Ud Din, Muhayy |
| dc.contributor.author | Rosell Gratacòs, Jan |
| dc.contributor.author | Bukhari, Sohail |
| dc.contributor.author | Ahmad, Mansoor |
| dc.contributor.author | Qazi, Wajahat Mahmood |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
| dc.date.accessioned | 2021-02-09T11:26:36Z |
| dc.date.available | 2021-02-09T11:26:36Z |
| dc.date.issued | 2020 |
| dc.identifier.citation | Ud Din, M. [et al.]. A lightweight perception module for planning purposes. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1277-1280. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212008. |
| dc.identifier.isbn | 978-1-7281-8957-4 |
| dc.identifier.uri | http://hdl.handle.net/2117/337586 |
| dc.description | © 2020 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.description.abstract | Sensing is an essential component for robots to perform the manipulation tasks in real environments. This study proposes a lightweight deep-learning-based sensing modules which allows the robots to automatically model the workspace for manipulation planning. This sensing module is developed as a part of our ongoing manipulation planning framework. It will be used to enhance the sensing accuracy and make it capable of planning the manipulation tasks in real environments. The retrained model is further trained over commonly used objects to enhance the prediction accuracy. |
| dc.format.extent | 4 p. |
| dc.language.iso | eng |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject | Àrees temàtiques de la UPC::Informàtica::Robòtica |
| dc.subject.lcsh | Machine learning |
| dc.title | A lightweight perception module for planning purposes |
| dc.type | Conference lecture |
| dc.subject.lemac | Robots -- Sistemes de control |
| dc.subject.lemac | Aprenentatge automàtic |
| dc.contributor.group | Universitat Politècnica de Catalunya. SIR - Service and Industrial Robotics |
| dc.identifier.doi | 10.1109/ETFA46521.2020.9212008 |
| dc.description.peerreviewed | Peer Reviewed |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9212008 |
| dc.rights.access | Open Access |
| local.identifier.drac | 29706834 |
| dc.description.version | Postprint (author's final draft) |
| dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/1PE/DPI2016-80077-R |
| local.citation.author | Ud Din, M.; Rosell, J.; Bukhari, S.; Ahmad, M.; Qazi, W. |
| local.citation.contributor | IEEE International Conference on Emerging Technologies and Factory Automation |
| local.citation.publicationName | 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020 |
| local.citation.startingPage | 1277 |
| local.citation.endingPage | 1280 |
