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Evaluation of feature representation and machine learning methods in grasp stability learning
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
2010 (English)In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 2010, 112-117 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of sensor-based grasping under uncertainty, specifically, the on-line estimation of grasp stability. We show that machine learning approaches can to some extent detect grasp stability from haptic pressure and finger joint information. Using data from both simulations and two real robotic hands, the paper compares different feature representations and machine learning methods to evaluate their performance in determining the grasp stability. A boosting classifier was found to perform the best of the methods tested.

Place, publisher, year, edition, pages
2010. 112-117 p.
Keyword [en]
Feature representation, Finger joints, Machine learning methods, Machine-learning, On-line estimation, Robotic hands, Anthropomorphic robots, Stability, Learning systems
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-150084DOI: 10.1109/ICHR.2010.5686310Scopus ID: 2-s2.0-79851498718ISBN: 978-142448688-5 (print)OAI: oai:DiVA.org:kth-150084DiVA: diva2:741923
Conference
2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 6 December 2010 through 8 December 2010, Nashville, TN, United States
Note

QC 20140829

Available from: 2014-08-29 Created: 2014-08-29 Last updated: 2014-08-29Bibliographically approved

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Kragic, Danica

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CiteExportLink to record
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Citation style
  • apa
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  • ieee
  • modern-language-association-8th-edition
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More styles
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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