Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization
File(s)
Accepted version
Author(s)
Shen, H
Yosinski, J
Kormushev, Petar
Caldwell, DG
Lipson, H
Type
Journal Article
Abstract
Legged robots are uniquely privileged over their wheeled counterparts in their potential to access rugged terrain. However, designing walking gaits by hand for legged robots is a difficult and time-consuming process, so we seek algorithms for learning such gaits to automatically using real world experimentation. Numerous previous studies have examined a variety of algorithms for learning gaits, using an assortment of different robots. It is often difficult to compare the algorithmic results from one study to the next, because the conditions and robots used vary. With this in mind, we have used an open-source, 3D printed quadruped robot called QuadraTot, so the results may be verified, and hopefully improved upon, by any group so desiring. Because many robots do not have accurate simulators, we test gait-learning algorithms entirely on the physical robot. Previous studies using the QuadraTot have compared parameterized splines, the HyperNEAT generative encoding and genetic algorithm. Among these, the research on the genetic algorithm was conducted by (G l e t t e et al., 2012) in a simulator and tested on a real robot. Here we compare these results to an algorithm called Policy learning by Weighting Exploration with the Returns, or RL PoWER. We report that this algorithm has learned the fastest gait through only physical experiments yet reported in the literature, 16.3% faster than reported for HyperNEAT. In addition, the learned gaits are less taxing on the robot and more repeatable than previous record-breaking gaits.
Date Issued
2012
Date Acceptance
2013-03-22
Citation
International Journal of Cybernetics and Information Technologies, 2012, 12
ISSN
1311-9702
Publisher
De Gruyter
Start Page
66
End Page
75
Journal / Book Title
International Journal of Cybernetics and Information Technologies
Volume
12
Issue
3
Copyright Statement
© 2013, Walter de Gruyter GmbH. This Open Access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivative Works Unported License (CC BY-NC-ND) which permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.
Identifier
http://kormushev.com/papers/Shen_CIT-2012.pdf
Publication Status
Published
Publisher URL
Article Number
3