@incollection{LNAI13-Barrett,
author = {Samuel Barrett and Katie Genter and Yuchen He and Todd Hester and Piyush Khandelwal and Jacob Menashe and Peter Stone},
title = {UT Austin Villa 2012: Standard Platform League World Champions},
booktitle= "RoboCup-2012: Robot Soccer World Cup {XVI}",
Editor={Xiaoping Chen and Peter Stone and Luis Enrique Sucar and Tijn Van der Zant},
Publisher="Springer Verlag",
address="Berlin",
year="2013",
series="Lecture Notes in Artificial Intelligence",
abstract= {
In 2012, UT Austin Villa claimed Standard Platform League
championships at both the US Open and RoboCup 2012 in Mexico
City. This paper describes the key contributions that led to the team's
victories. First, UT Austin Villa's code base was developed on a solid
foundation with a flexible architecture that enables easy testing
and debugging of code.  Next, the vision code was updated this year to
take advantage of the dual cameras and better processor of the new V4
Nao robots. To improve localization, a custom localization
simulator allowed us to implement and test a full team solution to
the challenge of both goals being the same color. The 2012 team made
use of Northern Bites' port of B-Human's walk engine, combined with
novel kicks from the walk. Finally, new behaviors and
strategies take advantage of opportunities for the robot to take time to
setup for a long kick, but kick very quickly when opponent robots are
nearby. The combination of these contributions led to the team's victories
in 2012.},
url="http://www.cs.utexas.edu/users/piyushk/papers/LNAI13-Barrett.pdf",
links="<a href=http://www.cs.utexas.edu/~AustinVilla/?p=downloads/source_code_and_binaries>[Code]</a>"
}

@InProceedings{ITSC2012-dcarlino,
author = {Dustin Carlino and Mike Depinet and Piyush Khandelwal and Peter Stone},
title = {Approximately Orchestrated Routing and Transportation Analyzer: Large-scale Traffic Simulation for Autonomous Vehicles},
booktitle = {Proceedings of the 15th IEEE Intelligent Transportation Systems Conference (ITSC 2012)},
location = {Anchorage, Alaska, USA},
month = {September},
year = {2012},
abstract = {Autonomous vehicles have seen great advancements in recent years, and such
vehicles are now closer than ever to being commercially available. The advent of
driverless cars provides opportunities for optimizing traffic in ways not
possible before. This paper introduces an open source multiagent microscopic
traffic simulator called AORTA, which stands for Approximately
Orchestrated Routing and Transportation Analyzer, designed for optimizing
autonomous traffic at a city-wide scale. AORTA creates scale simulations of the
real world by generating maps using publicly available road data from
OpenStreetMap (OSM). This allows simulations to be set up through
AORTA for a desired region anywhere in the world in a matter of minutes. AORTA
allows for traffic optimization by creating intelligent behaviors for
individual driver agents and intersection policies to be followed by these
agents. These behaviors and policies define how agents interact with one
another, control when they cross intersections, and route agents to their
destination. This paper demonstrates a simple application using AORTA through
an experiment testing intersection policies at a city-wide scale.},
url="http://www.cs.utexas.edu/users/piyushk/papers/itsc2012-dcarlino.pdf",
links="<a href=http://code.google.com/p/road-rage/>[Code]</a> <a href=http://www.cs.utexas.edu/users/piyushk/papers/itsc2012-dcarlino-slides.pdf>[Slides]</a>"
}

@article{GECCO12-Hausknecht,
title={HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player},
author={Matthew Hausknecht and Piyush Khandelwal and Risto Miikkulainen
and Peter Stone},
booktitle={Genetic and Evolutionary Computation Conference (GECCO) 2012},
url="http://www.cs.utexas.edu/users/piyushk/papers/GECCO12-Hausknecht.pdf",
year={2012},
abstract={This paper considers the challenge of enabling agents to
learn with as little domain-specic knowledge as possible.
The main contribution is HyperNEAT-GGP, a HyperNEAT-
based General Game Playing approach to Atari games. By
leveraging the geometric regularities present in the Atari
game screen, HyperNEAT eectively evolves policies for play-
ing two dierent Atari games, Asterix and Freeway. Results
show that HyperNEAT-GGP outperforms existing bench-
marks on these games. HyperNEAT-GGP represents a step
towards the ambitious goal of creating an agent capable of
learning and seamlessly transitioning between many dier-
ent tasks.},
links="<a href=http://www.cs.utexas.edu/users/piyushk/papers/GECCO12-Hausknecht-slides.pdf>[Slides]</a>"
}

@incollection{LNAI11-piyush,
author = {Piyush Khandelwal and Peter Stone},
title = {A Low Cost Ground Truth Detection System Using the Kinect},
booktitle= "{R}obo{C}up-2011: Robot Soccer World Cup {XV}",
Editor={Thomas Roefer and Norbert Michael Mayer and Jesus Savage
and Uluc Saranli},
Publisher="Springer Verlag",
address="Berlin",
year="2012",
series="Lecture Notes in Artificial Intelligence",
abstract = {Ground truth detection systems can be a crucial step in
evaluating and improving algorithms for self-localization on mobile
robots. Selecting a ground truth system depends on its cost, as well
as on the detail and accuracy of the information it provides. In this
paper, we present a low cost, portable and real-time solution
constructed using the Microsoft Kinect RGB-D Sensor. We use this
system to find the location of robots and the orange ball in the
Standard Platform League (SPL) environment in the RoboCup competition.
This system is fairly easy to calibrate, and does not require any
special identifiers on the robots. We also provide a detailed
experimental analysis to measure the accuracy of the data provided by
this system. Although presented for the SPL, this system can be adapted
for use with any indoor structured environment where ground truth
information is required.},
url="http://www.cs.utexas.edu/users/piyushk/papers/LNAI11-piyush.pdf",
links="<a href=http://www.ros.org/wiki/austinvilla>[Code]</a> <a href=http://www.cs.utexas.edu/users/piyushk/papers/LNAI11-piyush-poster.pdf>[Poster]</a>",
}

@InProceedings{HUMANOIDS10-khandelwal,
author = "Piyush Khandelwal and Matthew Hausknecht and Juhyun Lee
and Aibo Tian and Peter Stone",
title = "Vision Calibration and Processing on a Humanoid Soccer Robot",
booktitle = "The Fifth Workshop on Humanoid Soccer Robots (Humanoids
2010)",
location = "Nashville, TN",
month = "December",
year = "2010",
abstract = {In RoboCup, the problem of quickly and accurately
processing visual data continues to pose a significant challenge.
The Aldebaran Nao, currently used by the Standard Platform League,
has two cameras for visual input, of which only one has been typically
used. The integration of both cameras presents a new opportunity but
also a challenge. While it is possible to obtain better information
using both cameras, more cameras require more work to calibrate. We
propose a novel camera calibration algorithm which automatically tunes
a camera such that its color perceptions match those of another camera.
Additionally, recent vision challenges introduced in RoboCup have
necessitated the use of higher resolution images. We build on existing
work in color based segmentation and present novel extensions to
facilitate the move to higher resolution images, including memory
optimizations, fast line and curve detection, and differentiation via
robot pose based transformations. All work presented in this paper was
successfully used by the UT Austin Villa Robot Soccer team, which
secured 3rd place overall and 2nd place in the technical challenges at
RoboCup 2010.},
url="http://www.cs.utexas.edu/users/piyushk/papers/HUMANOIDS10-khandelwal.pdf",
}

@TechReport{UTAITR1201-sbarrett,
author="Samuel Barrett and Katie Genter and Todd Hester and Piyush
Khandelwal and Michael Quinlan and Peter Stone and Mohan Sridharan",
title="{A}ustin {V}illa 2011: Sharing is Caring: Better Awareness
through Information Sharing",
institution="The University of Texas at Austin, Department of Computer
Sciences, AI Laboratory",
number="UT-AI-TR-12-01",
year="2012",
month="January",
url="http://www.cs.utexas.edu/users/piyushk/papers/UTAITR1201-sbarrett.pdf",
note="Technical Report.",
abstract = {In 2008, UT Austin Villa entered a team in the first Nao competition of the
Standard Platform League of the RoboCup competition. The team had previous
experience in RoboCup in the Aibo leagues. Using this past experience, the team
developed an entirely new codebase for the Nao. In 2009, UT Austin combined
forces with Texas Tech University, to form TT-UT Austin Villa. Austin Villa
won the 2009 US Open and placed fourth in the 2009 RoboCup competition
in Graz, Austria. In 2010 Austin Villa successfully defended our 1st place at
the 2010 US Open and improved to finish 3rd at RoboCup 2010 in Singapore.
Austin Villa reached the quarterfinals at RoboCup 2011 in Istanbul, Turkey
before falling to the eventual champions, B-Human. This report describes the
algorithms used in these tournaments, including the architecture, vision, motion,
localization, and behaviors.},
}

@TechReport{UTAITR1101-spl10,
author="Samuel Barrett and Katie Genter and Matthew Hausknecht and
Todd Hester and Piyush Khandelwal and Juhyun Lee and Michael Quinlan
and Aibo Tian and Peter Stone and Mohan Sridharan",
title="{A}ustin {V}illa 2010 Standard Platform Team Report",
institution="The University of Texas at Austin, Department of Computer
Sciences, AI Laboratory",
number="UT-AI-TR-11-01",
year="2011",
month="January", 
abstract={In 2008, UT Austin Villa entered a team in the first
Nao competition of the Standard Platform League of the RoboCup
competition. The team had previous experience in RoboCup in the Aibo
leagues. Using this past experience, the team developed an entirely new
codebase for the Nao. In 2009, UT Austin combined forces with Texas Tech
University, to form TT-UT Austin Villa1. Austin Villa won the 2009 US
Open and placed fourth in the 2009 RoboCup competition in Graz, Austria.
In 2010 Austin Villa successfully defended our 1st place at the 2010 US
Open and improved to finish 3rd at RoboCup 2010 in Singapore.  This
report describes the algorithms used in these tournaments, including the
architecture, vision, motion, localization, and behaviors.},
url="http://www.cs.utexas.edu/users/piyushk/papers/UTAITR1101-spl10.pdf",
note="Technical Report.",
}