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Learning of Grasp Adaptation through Experience and Tactile Sensing
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.
KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP.ORCID-id: 0000-0003-2965-2953
2014 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2014.
Serie
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858 ; 6943027
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-165633DOI: 10.1109/IROS.2014.6943027ISI: 000349834603067Scopus ID: 2-s2.0-84911463955ISBN: 9781479969340 (tryckt)OAI: oai:DiVA.org:kth-165633DiVA, id: diva2:808728
Konferanse
IEEE/RSJ International Conference on Intelligent Robots and Systems
Merknad

QC 20150521

Tilgjengelig fra: 2015-04-29 Laget: 2015-04-29 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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  • de-DE
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