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A probabilistic framework for task-oriented grasp stability assessment
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.
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
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2013, 3040-3047 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. 3040-3047 p.
Series
Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keyword [en]
Generative model, Geometric constraint, Grasp stabilities, Object grasping, Probabilistic framework, Stability assessment, Stability requirements, Task information
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-139940DOI: 10.1109/ICRA.2013.6630999ISI: 000337617303008Scopus ID: 2-s2.0-84887277768ISBN: 978-146735641-1 (print)OAI: oai:DiVA.org:kth-139940DiVA: diva2:687822
Conference
2013 IEEE International Conference on Robotics and Automation, ICRA 2013, 6 May 2013 through 10 May 2013, Karlsruhe
Note

QC 20140115

Available from: 2014-01-15 Created: 2014-01-15 Last updated: 2014-08-04Bibliographically approved

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Kragic, Danica

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Citation style
  • apa
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Output format
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