Peter Stone's Selected Publications

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Autonomous Learning of Stable Quadruped Locomotion

Manish Saggar, Thomas D'Silva, Nate Kohl, and Peter Stone. Autonomous Learning of Stable Quadruped Locomotion. 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.
Some videos referenced in the paper.

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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.
        },
        wwwnote={<b>BEST PAPER AWARD NOMINEE</b> at RoboCup International Symposium.<br> Some <a href="http://www.cs.utexas.edu/~AustinVilla/?p=research/learned_walk">videos</a> referenced in the paper.},
)

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