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Assessing Grasp Stability Based on Learning and Haptic Data
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.
Department of Information Technology, Lappeenranta University of Technology, Finland.
The Maersk Mc-Kinney Moller Institute University of Southern Denmark, Denmark.
the Department of Information Technology, Lappeenranta University of Technology, Finland.
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2011 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 27, no 3, 616-629 p.Article in journal (Refereed) Published
Abstract [en]

An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machinelearning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements fromfingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research.

Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2011. Vol. 27, no 3, 616-629 p.
Keyword [en]
Force and tactile sensing, grasping, learning and adaptive systems, Hidden Markov models, Stability analysis, Support vector machines, Tactile sensors
National Category
Computer and Information Science Robotics
Identifiers
URN: urn:nbn:se:kth:diva-39069DOI: 10.1109/TRO.2011.2132870ISI: 000291404600020Scopus ID: 2-s2.0-79958770754OAI: oai:DiVA.org:kth-39069DiVA: diva2:439419
Projects
EU FP7 project CogX
Funder
ICT - The Next Generation
Note
QC 20110907Available from: 2011-09-07 Created: 2011-09-07 Last updated: 2017-12-08Bibliographically approved

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  • apa
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