Evaluation of feature representation and machine learning methods in grasp stability learning
2010 (English)In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 2010, 112-117 p.Conference paper (Refereed)
This paper addresses the problem of sensor-based grasping under uncertainty, specifically, the on-line estimation of grasp stability. We show that machine learning approaches can to some extent detect grasp stability from haptic pressure and finger joint information. Using data from both simulations and two real robotic hands, the paper compares different feature representations and machine learning methods to evaluate their performance in determining the grasp stability. A boosting classifier was found to perform the best of the methods tested.
Place, publisher, year, edition, pages
2010. 112-117 p.
Feature representation, Finger joints, Machine learning methods, Machine-learning, On-line estimation, Robotic hands, Anthropomorphic robots, Stability, Learning systems
IdentifiersURN: urn:nbn:se:kth:diva-150084DOI: 10.1109/ICHR.2010.5686310ScopusID: 2-s2.0-79851498718ISBN: 978-142448688-5OAI: oai:DiVA.org:kth-150084DiVA: diva2:741923
2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, 6 December 2010 through 8 December 2010, Nashville, TN, United States
QC 201408292014-08-292014-08-292014-08-29Bibliographically approved