Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Extracting Postural Synergies for Robotic Grasping
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: 0000-0002-5750-9655
Show others and affiliations
2013 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 29, no 6, 1342-1352 p.Article in journal (Refereed) Published
Abstract [en]

We address the problem of representing and encoding human hand motion data using nonlinear dimensionality reduction methods. We build our work on the notion of postural synergies being typically based on a linear embedding of the data. In addition to addressing the encoding of postural synergies using nonlinear methods, we relate our work to control strategies of combined reaching and grasping movements. We show the drawbacks of the (commonly made) causality assumption and propose methods that model the data as being generated from an inferred latent manifold to cope with the problem. Another important contribution is a thorough analysis of the parameters used in the employed dimensionality reduction techniques. Finally, we provide an experimental evaluation that shows how the proposed methods outperform the standard techniques, both in terms of recognition and generation of motion patterns.

Place, publisher, year, edition, pages
2013. Vol. 29, no 6, 1342-1352 p.
Keyword [en]
Grasping, humanoid robots, motion analysis, multifingered hand
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-140157DOI: 10.1109/TRO.2013.2272249ISI: 000328056400002Scopus ID: 2-s2.0-84897658190OAI: oai:DiVA.org:kth-140157DiVA: diva2:689508
Funder
EU, FP7, Seventh Framework Programme, FP7-ERC-279933 IST-FP7-270436Swedish Foundation for Strategic Research
Note

QC 20140121

Available from: 2014-01-21 Created: 2014-01-17 Last updated: 2017-12-06Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Kjellström, HedvigKragic, Danica

Search in DiVA

By author/editor
Ek, Carl HenrikKjellström, HedvigKragic, Danica
By organisation
Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
In the same journal
IEEE Transactions on robotics
Robotics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 128 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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