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Learning of Grasp Adaptation through Experience and Tactile Sensing
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

To perform robust grasping, a multi-fingered robotic hand should be able to adapt its grasping configuration, i.e., how the object is grasped, to maintain the stability of the grasp. Such a change of grasp configuration is called grasp adaptation and it depends on the controller, the employed sensory feedback and the type of uncertainties inherit to the problem. This paper proposes a grasp adaptation strategy to deal with uncertainties about physical properties of objects, such as the object weight and the friction at the contact points. Based on an object-level impedance controller, a grasp stability estimator is first learned in the object frame. Once a grasp is predicted to be unstable by the stability estimator, a grasp adaptation strategy is triggered according to the similarity between the new grasp and the training examples. Experimental results demonstrate that our method improves the grasping performance on novel objects with different physical properties from those used for training.

Place, publisher, year, edition, pages
2014.
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858 ; 6943027
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-165633DOI: 10.1109/IROS.2014.6943027ISI: 000349834603067Scopus ID: 2-s2.0-84911463955ISBN: 9781479969340 (print)OAI: oai:DiVA.org:kth-165633DiVA: diva2:808728
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems
Note

QC 20150521

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2017-03-30Bibliographically approved

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Kragic, Danica

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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