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abstract = {We present a probabilistic approach to learn robust
models of human motion through imitation. The association
of Hidden Markov Model (HMM), Gaussian Mixture Regression
(GMR) and dynamical systems allows us to extract
redundancies across multiple demonstrations and build
time-independent models to reproduce the dynamics of the
demonstrated movements. The approach is first
systematically evaluated and compared with other
approaches by using generated trajectories sharing
similarities with human gestures. Three applications on
different types of robots are then presented. An
experiment with the iCub humanoid robot acquiring a
bimanual dancing motion is first presented to show that
the system can also handle cyclic motion. An experiment
with a 7 DOFs WAM robotic arm learning the motion of
hitting a ball with a table tennis racket is presented to
highlight the possibility to encode several variations of
a movement in a single model. Finally, an experiment with
a HOAP-3 humanoid robot learning to manipulate a spoon to
feed the Robota humanoid robot is presented to
demonstrate the capability of the system to handle
several constraints simultaneously.},
affiliation = {EPFL},
author = {Calinon, Sylvain and D'halluin, Florent and Sauser, Eric
and Caldwell, Darwin and Billard, Aude},
details = {http://infoscience.epfl.ch/record/147286},
documenturl = {http://infoscience.epfl.ch/record/147286/files/CalinonEtAl-RAM2010.pdf},
doi = {10.1109/MRA.2010.936947},
issn = {1070-9932},
journal = {{IEEE} {R}obotics and {A}utomation {M}agazine},
keywords = {Learning by imitation; Dynamical Systems; Gaussian Mixture Models},
number = {2},
oai-id = {oai:infoscience.epfl.ch:147286},
oai-set = {article; fulltext-public; fulltext},
pages = {44--54},
publisher = {Institute of Electrical and Electronics Engineers},
review = {REVIEWED},
status = {PUBLISHED},
submitter = {115671; 177386; 177386; 177386; 115671},
title = {Learning and reproduction of gestures by imitation: {A}n
approach based on {H}idden {M}arkov {M}odel and
{G}aussian {M}ixture {R}egression},
unit = {LASA},
volume = {17},
year = 2010

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