Machine Learning for Fast Quadrupedal Locomotion

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

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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.

BibTeX Entry

@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.},
)