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
Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment
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
2011 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies the viability of concurrentobject pose tracking and tactile sensing for assessing graspstability on a physical robotic platform. We present a kernellogistic-regression model of pose- and touch-conditional graspsuccess probability. Models are trained on grasp data whichconsist of (1) the pose of the gripper relative to the object,(2) a tactile description of the contacts between the objectand the fully-closed gripper, and (3) a binary descriptionof grasp feasibility, which indicates whether the grasp canbe used to rigidly control the object. The data is collectedby executing grasps demonstrated by a human on a roboticplatform composed of an industrial arm, a three-finger gripperequipped with tactile sensing arrays, and a vision-based objectpose tracking system. The robot is able to track the poseof an object while it is grasping it, and it can acquiregrasp tactile imprints via pressure sensor arrays mounted onits gripper’s fingers. We consider models defined on severalsubspaces of our input data – using tactile perceptions orgripper poses only. Models are optimized and evaluated with f-fold cross-validation. Our preliminary results show that stabilityassessments based on both tactile and pose data can providebetter rates than assessments based on tactile data alone.

Place, publisher, year, edition, pages
2011.
National Category
Computer Science Robotics
Identifiers
URN: urn:nbn:se:kth:diva-63799OAI: oai:DiVA.org:kth-63799DiVA: diva2:482672
Conference
IEEE ICRA 2011 workshop: Manipulation Under Uncertainty, Shanghai, China, May 13th 2011
Note
QC 20120416Available from: 2012-01-24 Created: 2012-01-24 Last updated: 2012-04-16Bibliographically approved

Open Access in DiVA

No full text

Other links

http://www.csc.kth.se/~detryr/publications/Bekiroglu-2011-MUU.pdf

Authority records BETA

Kragic, Danica

Search in DiVA

By author/editor
Bekiroglu, YaseminDetry, RenaudKragic, Danica
By organisation
Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
Computer ScienceRobotics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 60 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