Autonomous Learning of Stable Quadruped Locomotion
Autonomous Learning of Stable Quadruped Locomotion.
Manish
Saggar, Thomas D'Silva, Nate Kohl, and Peter
Stone.
In Gerhard Lakemeyer, Elizabeth Sklar, Domenico Sorenti, and
Tomoichi Takahashi, editors, RoboCup-2006: Robot Soccer World Cup X, Lecture Notes in Artificial Intelligence, pp.
98–109, Springer Verlag, Berlin, 2007.
BEST PAPER AWARD NOMINEE at RoboCup International Symposium.
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Abstract
A fast gait is an essential component of any successful team in the RoboCup 4-legged league. However, quickly moving quadruped robots, including those with learned gaits, often move in such a way so as to cause unsteady camera motions which degrade the robot's visual capabilities. This paper presents an implementation of the policy gradient machine learning algorithm that searches for a parameterized walk while optimizing for both speed and stability. To the best of our knowledge, previous learned walks have all focused exclusively on speed. Our method is fully implemented and tested on the Sony Aibo ERS-7 robot platform. The resulting gait is reasonably fast and considerably more stable compared to our previous fast gaits. We demonstrate that this stability can significantly improve the robot's visual object recognition.
BibTeX Entry
@incollection(LNAI2006-manish,
author="Manish Saggar and Thomas D'Silva and Nate Kohl and Peter Stone",
title="Autonomous Learning of Stable Quadruped Locomotion",
booktitle= "{R}obo{C}up-2006: Robot Soccer World Cup {X}",
Editor="Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi",
Publisher="Springer Verlag",address="Berlin",year="2007",
issn="0302-9743",
isbn="978-3-540-74023-0",
series="Lecture Notes in Artificial Intelligence",
volume="4434",
pages="98--109",
abstract={
A fast gait is an essential component of any
successful team in the RoboCup 4-legged league.
However, quickly moving quadruped robots, including
those with learned gaits, often move in such a way
so as to cause unsteady camera motions which degrade
the robot's visual capabilities. This paper
presents an implementation of the policy gradient
machine learning algorithm that searches for a
parameterized walk while optimizing for both speed
and stability. To the best of our knowledge,
previous learned walks have all focused exclusively
on speed. Our method is fully implemented and
tested on the Sony Aibo ERS-7 robot platform. The
resulting gait is reasonably fast and considerably
more stable compared to our previous fast gaits. We
demonstrate that this stability can significantly
improve the robot's visual object recognition.
},
)
