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Machine Learning for Fast Quadrupedal Locomotion.
Nate Kohl and Peter
Stone.
In The Nineteenth National Conference on Artificial Intelligence, pp. 611–616, July 2004.
Some videos of walking robots referenced in
the paper.
AAAI 2004
[PDF]210.0kB [postscript]1.4MB
For a robot, the ability to get from one place to another is one of the most basic skills. However, locomotion on legged robots is a challenging multidimensional control problem. This paper presents a machine learning approach to legged locomotion, with all training done on the physical robots. The main contributions are a specification of our fully automated learning environment and a detailed empirical comparison of four different machine learning algorithms for learning quadrupedal locomotion. The resulting learned walk is considerably faster than all previously reported hand-coded walks for the same robot platform.
@InProceedings(AAAI04,
author="Nate Kohl and Peter Stone",
title="Machine Learning for Fast Quadrupedal Locomotion",
year="2004",month="July",
booktitle="The Nineteenth National Conference on Artificial Intelligence",
pages="611--616",
abstract={
For a robot, the ability to get from one place to
another is one of the most basic skills. However,
locomotion on legged robots is a challenging
multidimensional control problem. This paper
presents a machine learning approach to legged
locomotion, with all training done on the physical
robots. The main contributions are a specification
of our fully automated learning environment and a
detailed empirical comparison of four different
machine learning algorithms for learning quadrupedal
locomotion. The resulting learned walk is
considerably faster than all previously reported
hand-coded walks for the same robot platform.
},
wwwnote={Some <a href="http://www.cs.utexas.edu/users/AustinVilla/legged/learned-walk/">videos of walking robots</a> referenced in the paper.<br>
<a href="http://www.aaai.org/Conferences/National/2004/aaai04.html">AAAI 2004</a>},
)
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