Representations for Object Grasping and Learning from Experience
2010 (English)In: IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010, 1566-1571 p.Conference paper (Refereed)
We study two important problems in the area of robot grasping: i) the methodology and representations for grasp selection on known and unknown objects, and ii) learning from experience for grasping of similar objects. The core part of the paper is the study of different representations necessary for implementing grasping tasks on objects of different complexity. We show how to select a grasp satisfying force-closure, taking into account the parameters of the robot hand and collision-free paths. Our implementation takes also into account efficient computation at different levels of the system regarding representation, description and grasp hypotheses generation.
Place, publisher, year, edition, pages
2010. 1566-1571 p.
, IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Collision-free paths, Core part, Efficient computation, Force-closure, Hypotheses generation, Object grasping, Robot grasping, Robot hand, Unknown objects
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-32006DOI: 10.1109/IROS.2010.5648993ISI: 000287672000127ISBN: 978-1-4244-6675-7OAI: oai:DiVA.org:kth-32006DiVA: diva2:409171
IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, TAIWAN, OCT 18-22, 2010
QC 201104072011-04-072011-04-042011-04-07Bibliographically approved