Abstract One key idea behind morphological computation
is that many difficulties of a control problem can be
absorbed by the morphology of a robot. The performance
of the controlled system naturally depends on the
control architecture and on the morphology of the
robot. Because of this strong coupling, most of the
impressive applications in morphological computation
typically apply minimalistic control architectures.
Ideally, adapting the morphology of the plant and
optimizing the control law interact so that finally,
optimal physical properties of the system and optimal
control laws emerge. As a first step toward this
vision, we apply optimal control methods for
investigating the power of morphological computation.
We use a probabilistic optimal control method to
acquire control laws, given the current morphology. We
show that by changing the morphology of our robot,
control problems can be simplified, resulting in
optimal controllers with reduced complexity and higher
performance. This concept is evaluated on a compliant
four-link model of a humanoid robot, which has to keep
balance in the presence of external pushes.
%0 Journal Article
%1 ruckert-stochastic-optimal-control-2012
%A RÃŒckert, Elmar A.
%A Neumann, Gerhard
%D 2012
%I MIT Press
%J Artificial Life
%K alife robotics
%N 1
%P 115--131
%R 10.1162/ARTL_a_00085
%T Stochastic Optimal Control Methods for Investigating
the Power of Morphological Computation
%U http://dx.doi.org/10.1162/ARTL_a_00085
%V 19
%X Abstract One key idea behind morphological computation
is that many difficulties of a control problem can be
absorbed by the morphology of a robot. The performance
of the controlled system naturally depends on the
control architecture and on the morphology of the
robot. Because of this strong coupling, most of the
impressive applications in morphological computation
typically apply minimalistic control architectures.
Ideally, adapting the morphology of the plant and
optimizing the control law interact so that finally,
optimal physical properties of the system and optimal
control laws emerge. As a first step toward this
vision, we apply optimal control methods for
investigating the power of morphological computation.
We use a probabilistic optimal control method to
acquire control laws, given the current morphology. We
show that by changing the morphology of our robot,
control problems can be simplified, resulting in
optimal controllers with reduced complexity and higher
performance. This concept is evaluated on a compliant
four-link model of a humanoid robot, which has to keep
balance in the presence of external pushes.
@article{ruckert-stochastic-optimal-control-2012,
abstract = {Abstract One key idea behind morphological computation
is that many difficulties of a control problem can be
absorbed by the morphology of a robot. The performance
of the controlled system naturally depends on the
control architecture and on the morphology of the
robot. Because of this strong coupling, most of the
impressive applications in morphological computation
typically apply minimalistic control architectures.
Ideally, adapting the morphology of the plant and
optimizing the control law interact so that finally,
optimal physical properties of the system and optimal
control laws emerge. As a first step toward this
vision, we apply optimal control methods for
investigating the power of morphological computation.
We use a probabilistic optimal control method to
acquire control laws, given the current morphology. We
show that by changing the morphology of our robot,
control problems can be simplified, resulting in
optimal controllers with reduced complexity and higher
performance. This concept is evaluated on a compliant
four-link model of a humanoid robot, which has to keep
balance in the presence of external pushes.},
added-at = {2013-03-19T13:03:30.000+0100},
author = {R{\~A}{\OE}ckert, Elmar A. and Neumann, Gerhard},
biburl = {https://www.bibsonomy.org/bibtex/24bdc7eb2471073550a2110773e791b35/mhwombat},
day = 27,
doi = {10.1162/ARTL_a_00085},
interhash = {ca9afa439b0bae4977eb154e68dd35a7},
intrahash = {4bdc7eb2471073550a2110773e791b35},
issn = {1064-5462},
journal = {Artificial Life},
keywords = {alife robotics},
month = nov,
number = 1,
pages = {115--131},
publisher = {MIT Press},
timestamp = {2016-07-12T19:25:30.000+0200},
title = {Stochastic Optimal Control Methods for Investigating
the Power of Morphological Computation},
url = {http://dx.doi.org/10.1162/ARTL_a_00085},
volume = 19,
year = 2012
}