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Integrating Grasp Planning with Online Stability Assessment using Tactile Sensing
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.
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
2011 (English)In: IEEE International Conference on Robotics and Automation, IEEE conference proceedings, 2011, 4750-4755 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents an integration of grasp planning and online grasp stability assessment based on tactile data. We show how the uncertainty in grasp execution posterior to grasp planning can be dealt with using tactile sensing and machine learning techniques. The majority of the state-of-the-art grasp planners demonstrate impressive results in simulation. However, these results are mostly based on perfect scene/object knowledge allowing for analytical measures to be employed. It is questionable how well these measures can be used in realistic scenarios where the information about the object and robot hand may be incomplete and/or uncertain. Thus, tactile and force-torque sensory information is necessary for successful online grasp stability assessment. We show how a grasp planner can be integrated with a probabilistic technique for grasp stability assessment in order to improve the hypotheses about suitable grasps on different types of objects. Experimental evaluation with a three-fingered robot hand equipped with tactile array sensors shows the feasibility and strength of the integrated approach.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011. 4750-4755 p.
Keyword [en]
Grasping, Hidden Markov models, Stability analysis, Tactile sensors
National Category
Computer Science Robotics
Identifiers
URN: urn:nbn:se:kth:diva-46729DOI: 10.1109/ICRA.2011.5980049ISI: 000324383403154Scopus ID: 2-s2.0-84871710383ISBN: 978-1-61284-386-5 (print)OAI: oai:DiVA.org:kth-46729DiVA: diva2:454091
Conference
IEEE International Conference on Robotics and Automation. Shanghai, China. 9-13 May 2011
Projects
EU FP7 project CogX
Note

QC 20140930

Available from: 2011-11-04 Created: 2011-11-04 Last updated: 2014-09-30Bibliographically approved

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Kragic, Danica

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