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abstract = {Motion imitation requires reproduction of a dynamical
signature of a movement, i.e. a robot should be able to
encode and reproduce a particular path together with a
specific velocity and/or an acceleration profile.
Furthermore, a human provides only few demonstrations,
that cannot cover all possible contexts in which the
robot will need to reproduce the motion autonomously.
Therefore, the encoding should be able to efficiently
generalize knowledge by generating similar motions in
unseen context. This work follows a recent trend in
Programming by Demonstration in which the dynamics of the
motion is learned. We present an algorithm to estimate
multivariate robot motions through a Mixture of
Gaussians. The strengths of the proposed encoding are
three-fold: i) it allows to generalize a motion to unseen
context; ii) it provides fast on-line replanning of the
motion in the face of spatio-temporal perturbations; iii)
it may embed different types of dynamics, governed by
different attractors. The generality of the method to
estimate arbitrary nonlinear motion dynamics is
demonstrated by accurately estimating a set of known
non-linear dynamical systems. The platformindependency
and real-time performance of the method are further
validated to learn the non-linear motion dynamics of
manipulation tasks with different robotic platforms. We
provide an experimental comparison of our approach with
an related state-of-the-art method.},
affiliation = {EPFL},
author = {Gribovskaya, Elena and Khansari Zadeh, S. M. and Billard, Aude},
details = {http://infoscience.epfl.ch/record/148817},
documenturl = {http://infoscience.epfl.ch/record/148817/files/IJRR_Motion_Learning.pdf},
doi = {NA},
issn = {0278-3649},
journal = {International {J}ournal of {R}obotics {R}esearch},
keywords = {Non-Linear Autonomous Dynamical Systems;; Learning by Imitation},
oai-id = {oai:infoscience.epfl.ch:148817},
oai-set = {article; fulltext-public; fulltext},
publisher = {SAGE Publications},
review = {REVIEWED},
status = {PUBLISHED},
submitter = {173137; 173137; 173137; 173137},
title = {Learning {N}onlinear {M}ultivariate {D}ynamics of
{M}otion in {R}obotic {M}anipulators [accepted]},
unit = {LASA},
year = 2010

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