Learning a dictionary of prototypical grasp-predicting parts from grasping experience
2013 (English)In: 2013 IEEE International Conference on Robotics and Automation (ICRA), New York: IEEE , 2013, 601-608 p.Conference paper (Refereed)
We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.
Place, publisher, year, edition, pages
New York: IEEE , 2013. 601-608 p.
, IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Robotics, Grasping, Grasping strategy, Object grasping, Prototypical grasp-predicting part, Dimensionality reduction
Computer Vision and Robotics (Autonomous Systems) Robotics
IdentifiersURN: urn:nbn:se:kth:diva-136374DOI: 10.1109/ICRA.2013.6630635ISI: 000337617300088ScopusID: 2-s2.0-84887312609ISBN: 978-1-4673-5641-1OAI: oai:DiVA.org:kth-136374DiVA: diva2:675933
2013 IEEE International Conference on Robotics and Automation, ICRA 2013; Karlsruhe; Germany; 6 May 2013 through 10 May 2013
FunderEU, FP7, Seventh Framework Programme, FP7-IP-027657Swedish Foundation for Strategic Research Swedish Research CouncilEU, FP7, Seventh Framework Programme, IST-FP7-270436
QC 201312162013-12-042013-12-042014-08-04Bibliographically approved