Trajectory learning for robot programming by demonstration using hidden markov model and dynamic time warping

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DOIResolve DOI: http://doi.org/10.1109/TSMCB.2012.2185694
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TypeArticle
Journal titleIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ISSN1083-4419
Volume42
Issue4
Article number6166903
Pages10391052; # of pages: 14
SubjectComplex trajectories; Dynamic time warping; Dynamic time warping algorithms; Keypoints; Programming by demonstration; Robot programming by demonstration; Smoothing spline; Weighting coefficient; Hidden Markov models; Programmable robots; Robot programming; Robotics; Trajectories
AbstractThe main objective of this paper is to develop an efficient method for learning and reproduction of complex trajectories for robot programming by demonstration. Encoding of the demonstrated trajectories is performed with hidden Markov model, and generation of a generalized trajectory is achieved by using the concept of key points. Identification of the key points is based on significant changes in position and velocity in the demonstrated trajectories. The resulting sequences of trajectory key points are temporally aligned using the multidimensional dynamic time warping algorithm, and a generalized trajectory is obtained by smoothing spline interpolation of the clustered key points. The principal advantage of our proposed approach is utilization of the trajectory key points from all demonstrations for generation of a generalized trajectory. In addition, variability of the key points' clusters across the demonstrated set is employed for assigning weighting coefficients, resulting in a generalization procedure which accounts for the relevance of reproduction of different parts of the trajectories. The approach is verified experimentally for trajectories with two different levels of complexity. © 2012 IEEE.
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LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); Aerospace (AERO-AERO)
Peer reviewedYes
NPARC number21269431
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Record identifier5b319383-e246-4e65-9912-3491ae7b4f4f
Record created2013-12-12
Record modified2016-05-09
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