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Learning grasp stability based on tactile data and HMMs
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
Department of Information Technology, Lappeenranta University of Technology, Finland.
2010 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2010. 132-137 p.
Keyword [en]
Grasping, Hidden Markov models, Stability analysis, Tactile sensors
National Category
Computer Science Robotics
Identifiers
URN: urn:nbn:se:kth:diva-46731DOI: 10.1109/ROMAN.2010.5598659ISI: 000300610200025Scopus ID: 2-s2.0-78649868536ISBN: 978-1-4244-7991-7 (print)OAI: oai:DiVA.org:kth-46731DiVA: diva2:454093
Conference
IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010
Projects
EU FP7 project CogX
Note

QC 20130605

Available from: 2011-11-04 Created: 2011-11-04 Last updated: 2013-06-05Bibliographically approved

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Kragic, Danica

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Output format
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