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
From object categories to grasp transfer using probabilistic reasoning
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
2012 (English)In: 2012 IEEE International Conference on Robotics and Automation (ICRA), IEEE Computer Society, 2012, 1716-1723 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we address the problem of grasp generation and grasp transfer between objects using categorical knowledge. The system is built upon an i) active scene segmentation module, able of generating object hypotheses and segmenting them from the background in real time, ii) object categorization system using integration of 2D and 3D cues, and iii) probabilistic grasp reasoning system. Individual object hypotheses are first generated, categorized and then used as the input to a grasp generation and transfer system that encodes task, object and action properties. The experimental evaluation compares individual 2D and 3D categorization approaches with the integrated system, and it demonstrates the usefulness of the categorization in task-based grasping and grasp transfer.

Place, publisher, year, edition, pages
IEEE Computer Society, 2012. 1716-1723 p.
Series
IEEE International Conference on Robotics and Automation, ISSN 2152-4092
Keyword [en]
Experimental evaluation, Individual objects, Integrated systems, Object categories, Object categorization, Probabilistic reasoning, Real time, Reasoning system, Scene segmentation, Task-based, Transfer systems, Robotics, Three dimensional
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-101528DOI: 10.1109/ICRA.2012.6225052ISI: 000309406701111Scopus ID: 2-s2.0-84864441408ISBN: 978-146731403-9 (print)OAI: oai:DiVA.org:kth-101528DiVA: diva2:549673
Conference
2012 IEEE International Conference on Robotics and Automation, RiverCentre, Saint Paul, Minnesota, USA, May 14-18, 2012
Funder
ICT - The Next Generation
Note

QC 20120905

Available from: 2012-09-05 Created: 2012-08-30 Last updated: 2013-10-02Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Kragic, Danica

Search in DiVA

By author/editor
Madry, MariannaSong, DanKragic, 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: 282 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