Minimum jerk based prediction of user actions for a ball catching task
2007 (English)In: IEEE International Conference on Intelligent Robots and Systems: Vols 1-9, IEEE conference proceedings, 2007, 2716-2722 p.Conference paper (Refereed)
The present paper examines minimum jerk models for human kinematics as a tool to predict user input in teleoperation with significant dynamics. Predictions of user input can be a powerful tool to bridge time-delays and to trigger autonomous sub-sequences. In this paper an example implementation is presented, along with the results of a pilot experiment in which a virtual reality simulation of a teleoperated ball-catching scenario is used to test the predictive power of the model. The results show that delays up to 100 ms can potentially be bridged with this approach.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2007. 2716-2722 p.
prediction, minimum jerk, teleoperation
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-39181DOI: 10.1109/IROS.2007.4398989ISI: 000254073201217ScopusID: 2-s2.0-51349090120ISBN: 978-1-4244-0911-2OAI: oai:DiVA.org:kth-39181DiVA: diva2:439502
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007; San Diego, CA; 29 October 2007 through 2 November 2007
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QC 201109082012-01-112011-09-082012-01-11Bibliographically approved