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
Sparse Summarization of Robotic Grasping Data
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: 0000-0002-1031-9600
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: 0000-0002-5750-9655
Show others and affiliations
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, 1082-1087 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.

Place, publisher, year, edition, pages
New York: IEEE , 2013. 1082-1087 p.
Series
IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keyword [en]
Principal Component Analysis, Models
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-136187DOI: 10.1109/ICRA.2013.6630707ISI: 000337617301013Scopus ID: 2-s2.0-84887316002ISBN: 978-1-4673-5643-5 (print)ISBN: 978-1-4673-5641-1 (print)OAI: oai:DiVA.org:kth-136187DiVA: diva2:675562
Conference
2013 IEEE International Conference on Robotics and Automation, ICRA 2013; Karlsruhe; Germany; 6 May 2013 through 10 May 2013
Note

QC 20140129

Available from: 2013-12-04 Created: 2013-12-04 Last updated: 2014-08-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Hjelm, MartinKjellström, HedvigKragic, Danica

Search in DiVA

By author/editor
Hjelm, MartinEk, Carl HenrikDetry, RenaudKjellström, HedvigKragic, Danica
By organisation
Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 50 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