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In-Hand Manipulation of Objects with Unknown Shapes
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-9171-8768
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-3599-440X
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0003-2965-2953
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This work addresses the problem of changing grasp configurations on objects with an unknown shape through in-hand manipulation. Our approach leverages shape priors,learned as deep generative models, to infer novel object shapesfrom partial visual sensing. The Dexterous Manipulation Graph method is extended to build upon incremental data and account for estimation uncertainty in searching a sequence of manipulation actions. We show that our approach successfully solves in-hand manipulation tasks with unknown objects, and demonstrate the validity of these solutions with robot experiments.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-263201OAI: oai:DiVA.org:kth-263201DiVA, id: diva2:1367290
Note

QC 20191029

Available from: 2019-11-02 Created: 2019-11-02 Last updated: 2019-11-26Bibliographically approved

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fulltext(375 kB)2 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf