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abstract = {We present a probabilistic architecture for solving
generically the problem of extracting the task
constraints through a Programming by Demonstration (PbD)
framework and for generalizing the acquired knowledge to
various situations. In previous work, we proposed an
approach based on Gaussian Mixture Regression (GMR) to
find a controller for the robot reproducing the
statistical characteristics of a movement in joint space
and in task space through Lagrange optimization. In this
paper, we develop an alternative procedure to handle
simultaneously constraints in joint space and in task
space by combining directly the probabilistic
representation of the task constraints with a solution to
Jacobian-based inverse kinematics. The method is
validated in manipulation tasks with two 5 DOFs Katana
robotic arms displacing a set of objects.},
affiliation = {EPFL},
author = {Calinon, Sylvain and Billard, Aude},
details = {http://infoscience.epfl.ch/record/143499},
documenturl = {http://infoscience.epfl.ch/getfile.py?recid=143499&mode=best},
doi = {10.1163/016918609X12529294461843},
extra-id = {000272807100005},
journal = {Advanced {R}obotics},
keywords = {Robot programming by demonstration; Learning by
imitation; Inverse kinematics},
number = {15},
oai-id = {oai:infoscience.epfl.ch:143499},
oai-set = {article; fulltext-public; fulltext},
pages = {2059--2076},
review = {REVIEWED},
status = {PUBLISHED},
title = {Statistical {L}earning by {I}mitation of {C}ompeting
{C}onstraints in {J}oint {S}pace and {T}ask {S}pace},
unit = {LASA},
volume = {23},
year = 2009

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