Automatic Gait Optimization with Gaussian Process Regression
Gait optimization is a basic yet challenging problem
for both quadrupedal and bipedal robots. Although
techniques for automating the process exist,
most involve local function optimization procedures
that suffer from three key drawbacks. Local
optimization techniques are naturally plagued
by local optima, make no use of the expensive gait
evaluations once a local step is taken, and do not
explicitly model noise in gait evaluation. These
drawbacks increase the need for a large number
of gait evaluations, making optimization slow, data
inefficient, and manually intensive. We present
a Bayesian approach based on Gaussian process
regression that addresses all three drawbacks. It
uses a global search strategy based on a posterior
model inferred from all of the individual noisy
evaluations. We demonstrate the technique on a
quadruped robot, using it to optimize two different
criteria: speed and smoothness. We show in both
cases our technique requires dramatically fewer
gait evaluations than state-of-the-art local gradient
approaches.
Citation
D. Lizotte,
T. Wang,
M. Bowling,
D. Schuurmans.
"Automatic Gait Optimization with Gaussian Process Regression".
International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007.
| Keywords: |
Gaussian, optimization, machine learning |
| Category: |
In Conference |
BibTeX
@incollection{Lizotte+al:IJCAI07,
author = {Dan Lizotte and Tao Wang and Michael Bowling and Dale Schuurmans},
title = {Automatic Gait Optimization with Gaussian Process Regression},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2007,
}Last Updated: April 24, 2007
Submitted by Nelson Loyola 
