Grasp recognition for programming by demonstration
2005 (English)In: 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4, NEW YORK, NY: IEEE , 2005, 748-753 p.Conference paper (Refereed)
The demand for flexible and re-programmable robots has increased the need for programming by demonstration systems. In this paper, grasp recognition is considered in a programming by demonstration framework. Three methods for grasp recognition are presented and evaluated. The first method uses Hidden Markov Models to model the hand posture sequence during the grasp sequence, while the second method relies on the hand trajectory and hand rotation. The third method is a hybrid method, in which both the first two methods are active in parallel. The particular contribution is that all methods rely on the grasp sequence and not just the final posture of the hand. This facilitates grasp recognition before the grasp is completed. Also, by analyzing the entire sequence and not just the final grasp, the decision is based on more information and increased robustness of the overall system is achieved. The experimental results show that both arm trajectory and final hand posture provide important information for grasp classification. By combining them, the recognition rate of the overall system is increased.
Place, publisher, year, edition, pages
NEW YORK, NY: IEEE , 2005. 748-753 p.
, IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ISSN 1050-4729
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-43155ISI: 000235460100121ScopusID: 2-s2.0-33846129118ISBN: 0-7803-8914-XOAI: oai:DiVA.org:kth-43155DiVA: diva2:448203
IEEE International Conference on Robotics and Automation (ICRA). Barcelona, SPAIN. APR 18-22, 2005
QC 201110142011-10-142011-10-132012-01-24Bibliographically approved