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Using Dynamic Rewards to Learn a Fully Holonomic Bipedal Walk.
Patrick
MacAlpine and Peter Stone.
In AAMAS Adaptive Learning Agents (ALA)
Workshop, June 2012.
Video available at http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2012/html/holonomicwalk.html
[PDF]525.6kB [postscript]2.2MB [slides.pdf]159.5MB
This paper presents the design and learning architecture for a fully holonomic omnidirectional walk used by the UT Austin Villa humanoid robot soccer agent acting in the RoboCup 3D simulation environment. By ``fully holonomic'' we mean the walk allows for movement in all directions with equal velocity. The walk is based on a double linear inverted pendulum model and was originally designed for the actual physical Nao robot. Parameters for the walk are optimized for maximum speed and stability while at the same time a novel approach of reweighting rewards for walking speeds in the cardinal directions of forwards, backwards, and sideways is utilized to promote equal walking velocities in all directions. A variant of this walk which uses the same walk engine, but is not fully holonomic as it employs three different sets of learned walk parameters biased toward maximizing forward walking speed, was the crucial component in the UT Austin Villa team winning the 2011 RoboCup 3D simulation competition. Detailed experiments reveal that adaptively changing the weights of rewards over time is an effective method for learning a fully holonomic walk. Additional data shows that a team of agents using this learned fully holonomic walk is able to beat other teams, including that of the 2011 RoboCup 3D simulation champion UT Austin Villa team, that utilize non-fully holonomic walks.
@InProceedings{ALA12-MacAlpine,
author = {Patrick MacAlpine and Peter Stone},
title = {Using Dynamic Rewards to Learn a Fully Holonomic Bipedal Walk},
booktitle = {AAMAS Adaptive Learning Agents (ALA) Workshop},
location = {Valencia, Spain},
month = {June},
year = {2012},
abstract={
This paper presents the design and learning architecture for a fully
holonomic omnidirectional walk used by the UT Austin Villa humanoid robot
soccer agent acting in the RoboCup 3D simulation environment. By ``fully
holonomic'' we mean the walk allows for movement in all directions with equal
velocity. The walk is based on a double linear inverted pendulum model and was
originally designed for the actual physical Nao robot. Parameters for the walk
are optimized for maximum speed and stability while at the same time a novel
approach of reweighting rewards for walking speeds in the cardinal directions of
forwards, backwards, and sideways is utilized to promote equal walking
velocities in all directions. A variant of this walk which uses the same walk
engine, but is not fully holonomic as it employs three different sets of learned
walk parameters biased toward maximizing forward walking speed, was the crucial
component in the UT Austin Villa team winning the 2011 RoboCup 3D simulation
competition. Detailed experiments reveal that adaptively changing the weights
of rewards over time is an effective method for learning a fully holonomic walk.
Additional data shows that a team of agents using this learned fully holonomic
walk is able to beat other teams, including that of the 2011 RoboCup 3D
simulation champion UT Austin Villa team, that utilize non-fully holonomic
walks.
},
wwwnote={Video available at <a href="http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2012/html/holonomicwalk.html">http://www.cs.utexas.edu/~AustinVilla/sim/3dsimulation/AustinVilla3DSimulationFiles/2012/html/holonomicwalk.html</a>},
}
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