English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/96593
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

POMDP approach to robotized clothes separation

AuthorsMonsó, Pol ; Alenyà, Guillem ; Torras, Carme
Issue Date2012
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE/RSJ International Conference on Intelligent Robots and Systems: 1324-1329 (2012)
AbstractRigid object manipulation with robots has mainly relied on precise, expensive models and deterministic sequences. Given the great complexity of accurately modeling deformable objects, their manipulation seems to call for a rather different approach. This paper proposes a probabilistic planner, based on a Partially Observable Markov Decision Process (POMDP), targeted at reducing the inherent uncertainty of deformable object sorting. It is shown that a small set of unreliable actions and inaccurate perceptions suffices to accomplish the task, provided faithful statistics on both of them are collected beforehand. The planner has been applied to a clothes sorting task in a real case context with a depth and color sensor and a robotic arm. Experimental results show the promise of the approach since more than 95% certainty of having isolated a piece of clothing is reached in an average of four steps for quite entangled initial clothing configurations.
DescriptionTrabajo presentado al IROS celebrado en Vilamoura (Portugal) del 7 al 12 de octubre de 2012.
Publisher version (URL)http://dx.doi.org/10.1109/IROS.2012.6386011
URIhttp://hdl.handle.net/10261/96593
DOI10.1109/IROS.2012.6386011
Identifiersdoi: 10.1109/IROS.2012.6386011
isbn: 978-1-4673-1737-5
Appears in Collections:(IRII) Libros y partes de libros
Files in This Item:
File Description SizeFormat 
POMDP approach.pdf283,49 kBUnknownView/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.