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Machine Learning for Fast Quadrupedal Locomotion (2004)
Nate Kohl and
Peter Stone
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.
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Citation:
In
Nineteenth National Conference on Artificial Intelligence
, pp. 611-616, July 2004.
Bibtex:
@InProceedings{kohl:aaai04, title={Machine Learning for Fast Quadrupedal Locomotion}, author={Nate Kohl and Peter Stone}, booktitle={Nineteenth National Conference on Artificial Intelligence}, month={July}, pages={611-616}, url="http://www.cs.utexas.edu/users/ai-lab?kohl:aaai04", year={2004} }
People
Peter Stone
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pstone [at] cs utexas edu
Areas of Interest
Other Areas
Quadruped Robots
Reinforcement Learning
Labs
Learning Agents