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Estimating deformability of objects using meshless shape matching
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 5941-5948, article id 8206489Conference paper, Published paper (Refereed)
Abstract [en]

Humans interact with deformable objects on a daily basis but this still represents a challenge for robots. To enable manipulation of and interaction with deformable objects, robots need to be able to extract and learn the deformability of objects both prior to and during the interaction. Physics-based models are commonly used to predict the physical properties of deformable objects and simulate their deformation accurately. The most popular simulation techniques are force-based models that need force measurements. In this paper, we explore the applicability of a geometry-based simulation method called meshless shape matching (MSM) for estimating the deformability of objects. The main advantages of MSM are its controllability and computational efficiency that make it popular in computer graphics to simulate complex interactions of multiple objects at the same time. Additionally, a useful feature of the MSM that differentiates it from other physics-based simulation is to be independent of force measurements that may not be available to a robotic framework lacking force/torque sensors. In this work, we design a method to estimate deformability based on certain properties, such as volume conservation. Using the finite element method (FEM) we create the ground truth deformability for various settings to evaluate our method. The experimental evaluation shows that our approach is able to accurately identify the deformability of test objects, supporting the value of MSM for robotic applications.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 5941-5948, article id 8206489
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-224274DOI: 10.1109/IROS.2017.8206489Scopus ID: 2-s2.0-85041942624ISBN: 9781538626825 OAI: oai:DiVA.org:kth-224274DiVA, id: diva2:1190785
Conference
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Vancouver, Canada, 24 September 2017 through 28 September 2017
Funder
EU, FP7, Seventh Framework Programme, FP7-ICT-288533Swedish Foundation for Strategic Research
Note

QC 20180315

Available from: 2018-03-15 Created: 2018-03-15 Last updated: 2018-03-15Bibliographically approved

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Citation style
  • apa
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  • modern-language-association-8th-edition
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  • Other locale
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Output format
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