2015

  • Piyush Khandelwal, Samuel Barrett, and Peter Stone, “Leading the Way: An Efficient Multi-robot Guidance System,” in International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015.
    [Bibtex]
    @InProceedings{AAMAS15-khandelwal,
    author = {Piyush Khandelwal and Samuel Barrett and Peter Stone},
    title = {Leading the Way: An Efficient Multi-robot Guidance System},
    booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
    location = {Istanbul, Turkey},
    month = {May},
    year = {2015},
    abstract = {
    Recent advances in service robotics have made it possible to deploy a large
    number of mobile robots in indoor environments to perform tasks such as
    delivery, maintenance and eldercare. If a centrally connected multi-robot
    system is available, can it be effectively used to aid humans in other
    on-demand tasks? In this paper, we demonstrate how individual service
    robots in a multi-robot system can be temporarily reassigned from their
    original task to help guide a human from one location to another in the
    environment. We formulate this multi-robot treatment of the human guidance
    problem as a Markov Decision Process (MDP). Solving the MDP produces a
    policy to efficiently guide the human, but the state space size makes it
    infeasible to optimally solve it. Instead, we use the Upper Confidence
    bound for Trees (UCT) planner to obtain an approximate solution. We show
    that this solution outperforms an approach that uses a single robot to
    guide the human from start to finish.
    },
    url="http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/AAMAS15-khandelwal.pdf"
    }
  • Shiqi Zhang, Fankgai Yang, Piyush Khandelwal, and Peter Stone, “Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy,” in Proceedings of the 13th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR), 2015.
    [Bibtex]
    @InProceedings{LPNMR15-zhang,
    author = {Shiqi Zhang and Fangkai Yang and Piyush Khandelwal and Peter Stone},
    title = {Mobile Robot Planning using Action Language BC with an Abstraction Hierarchy},
    booktitle = {Proceedings of the 13th International Conference on Logic
    Programming and Non-monotonic Reasoning (LPNMR)},
    location = {Lexington, KY, USA},
    month = {September},
    year = {2015},
    abstract = {
    Planning in real-world environments can be challenging for intelligent
    robots due to incomplete domain knowledge that results from unpredictable
    domain dynamism, and due to lack of global observability. Action language BC
    can be used for planning by formalizing the preconditions and (direct and
    indirect) effects of actions, and is especially suited for planning in
    robotic domains by incorporating defaults with the incomplete domain
    knowledge. However, planning with BC is very computationally expensive,
    especially when action costs are considered. We introduce algorithm PlanHG
    for formalizing BC domains at different abstraction levels in order to trade
    optimality for significant efficiency improvement when aiming to minimize
    overall plan cost. We observe orders of magnitude improvement in efficiency
    compared to a standard “flat” planning approach.
    },
    url="http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/LPNMR15-zhang.pdf"
    links="<a href="https://youtu.be/-QpFj7BbiRU">[Demo Video]</a>",
    }

2014

  • Samuel Barrett, Katie Genter, Yuchen He, Todd Hester, Piyush Khandelwal, Jacob Menashe, and Peter Stone, “The 2012 UT Austin Villa Code Release,” in RoboCup-2013: Robot Soccer World Cup XVII, Berlin, 2014.
    [Bibtex]
    @InProceedings{LNAI13-BarrettCodeRelease,
    author = {Samuel Barrett and Katie Genter and Yuchen He and Todd Hester and Piyush Khandelwal and Jacob Menashe and Peter Stone},
    title = {The 2012 {UT Austin Villa} Code Release},
    booktitle = {{R}obo{C}up-2013: Robot Soccer World Cup {XVII}},
    Publisher="Springer Verlag",
    address="Berlin",
    year = {2014},
    series="Lecture Notes in Artificial Intelligence",
    abstract={
    In 2012, UT Austin Villa claimed the Standard Platform League championships at both the US Open and the 2012 RoboCup competition held in Mexico City. This paper describes the code release associated with the team and discusses the key contributions of the release. This release will enable teams entering the Standard Platform League and researchers using the Naos to have a solid foundation from which to start their work as well as providing useful modules to existing researchers and RoboCup teams. We expect it to be of particular interest because it includes the architecture, logic modules, and debugging tools that led to the team's success in 2012. This architecture is designed to be flexible and robust while enabling easy testing and debugging of code. The vision code was designed for easy use in creating color tables and debugging problems. A custom localization simulator that is included permits fast testing of full team scenarios. Also included is the kick engine which runs through a number of static joint poses and adapts them to the current location of the ball. This code release will provide a solid foundation for new RoboCup teams and for researchers that use the Naos.},
    url="http://www.cs.utexas.edu/users/piyushk/papers/LNAI13-BarrettCodeRelease.pdf",
    links="<a href=http://www.cs.utexas.edu/~AustinVilla/?p=downloads/source_code_and_binaries>[Code]</a>"
    }
  • Piyush Khandelwal, Fankgai Yang, Matteo Leonetti, Vladimir Lifschitz, and Peter Stone, “Planning in Action Language ${\cal BC}$ while Learning Action Costs for Mobile Robots,” in International Conference on Automated Planning and Scheduling (ICAPS), 2014.
    [Bibtex]
    @InProceedings{ICAPS2014-khandelwal,
    author = "Piyush Khandelwal and Fangkai Yang and Matteo Leonetti and Vladimir Lifschitz and Peter Stone",
    title = "Planning in Action Language ${\cal BC}$ while Learning Action Costs for Mobile Robots",
    booktitle = "International Conference on Automated Planning and Scheduling (ICAPS)",
    url="http://www.cs.utexas.edu/users/piyushk/papers/itsc2012-dcarlino.pdf",
    year = "2014",
    url="http://www.cs.utexas.edu/~pstone/Papers/bib2html-links/ICAPS14-khandelwal.pdf"
    }
  • Piyush Khandelwal and Peter Stone, “Multi-Robot Human Guidance using Topological Graphs,” in AAAI Spring Symposium on Qualitiative Representation for Robots, 2014.
    [Bibtex]
    @InProceedings{AAAIsymp14-khandelwal,
    author = "Piyush Khandelwal and Peter Stone",
    title = "Multi-Robot Human Guidance using Topological Graphs",
    booktitle = "AAAI Spring Symposium on Qualitiative Representation for Robots",
    year = "2014"
    }
  • Fankgai Yang, Piyush Khandelwal, Matteo Leonetti, and Peter Stone, “Planning in Answer Set Programming while Learning Action Costs for Mobile Robots,” in AAAI Spring Symposium on Knowledge Representation and Reasoning in Robotics, 2014.
    [Bibtex]
    @InProceedings{AAAIsymp14-yang,
    author = "Fangkai Yang and Piyush Khandelwal and Matteo Leonetti and Peter Stone",
    title = "Planning in Answer Set Programming while Learning Action Costs for Mobile Robots",
    booktitle = "AAAI Spring Symposium on Knowledge Representation and Reasoning in Robotics",
    year = "2014"
    }
  • Shiqi Zhang, Fankgai Yang, Piyush Khandelwal, and Peter Stone, “Mobile Robot Planning using Action Language ${\cal BC}$ with Hierarchical Domain Abstractions,” in The 7th Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP), 2014.
    [Bibtex]
    @InProceedings{ASPOCP14-zhang,
    author = {Shiqi Zhang and Fangkai Yang and Piyush Khandelwal and Peter Stone},
    title = {Mobile Robot Planning using Action Language ${\cal BC}$ with Hierarchical Domain Abstractions},
    booktitle = {The 7th Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP)},
    location = {Vienna, Austria},
    month = {July},
    year = {2014},
    abstract = {
    Action language ${\cal BC}$ provides an elegant way of formalizing robotic
    domains which need to be expressed using default logic as well as indirect
    and recursive action effects. However, generating plans efficiently for
    large domains using ${\cal BC}$ can be challenging, even when
    state-of-the-art answer set solvers are used. In this paper, we investigate
    the computational gains achieved by describing task planning domains at
    different abstraction levels using ${\cal BC}$, where lower levels describe
    more domain details by adding fluents not included in higher levels and
    actions at different levels are formalized independently. Two algorithms are
    presented to efficiently calculate the near-optimal short and low-cost plans
    respectively. We present a case study where at least an order of magnitude
    speedup was achieved in a robot mail collection task using hierarchical
    domain abstractions.
    },
    }

2013

  • Samuel Barrett, Katie Genter, Yuchen He, Todd Hester, Piyush Khandelwal, Jacob Menashe, and Peter Stone, “UT Austin Villa 2012: Standard Platform League World Champions,” in RoboCup-2012: Robot Soccer World Cup XVI, X. Chen, Peter Stone, L. E. Sucar, and T. V. der Zant, Eds., Berlin: Springer Verlag, 2013.
    [Bibtex]
    @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>"
    }

2012

  • Piyush Khandelwal and Peter Stone, “A Low Cost Ground Truth Detection System Using the Kinect,” in RoboCup-2011: Robot Soccer World Cup XV, T. Roefer, N. M. Mayer, J. Savage, and U. Saranli, Eds., Berlin: Springer Verlag, 2012.
    [Bibtex]
    @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>",
    }
  • Matthew Hausknecht, Piyush Khandelwal, Risto Miikkulainen, and Peter Stone, “Hyper-NEAT-GGP: A HyperNEAT-based Atari General Game Player,” in Genetic and Evolutionary Computation Conference (GECCO), 2012.
    [Bibtex]
    @InProceedings{GECCO12-Hausknecht,
    title={Hyper-NEAT-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)},
    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-specific 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 ectively evolves policies for play-
    ing two diㄦent 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 diㄦ-
    ent tasks.},
    links="<a href=http://www.cs.utexas.edu/users/piyushk/papers/GECCO12-Hausknecht-slides.pdf>[Slides]</a>"
    }
  • Dustin Carlino, Mike Depinet, Piyush Khandelwal, and Peter Stone, “Approximately Orchestrated Routing and Transportation Analyzer: Large-scale Traffic Simulation for Autonomous Vehicles,” in IEEE Intelligent Transportation Systems Conference (ITSC), 2012.
    [Bibtex]
    @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 = {IEEE Intelligent Transportation Systems Conference (ITSC)},
    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>"
    }
  • Samuel Barrett, Katie Genter, Todd Hester, Piyush Khandelwal, Michael Quinlan, Peter Stone, and Mohan Sridharan, “Austin Villa 2011: Sharing is Caring: Better Awareness through Information Sharing,” The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, UT-AI-TR-12-01, 2012.
    [Bibtex]
    @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.},
    }

2011

  • Samuel Barrett, Katie Genter, Matthew Hausknecht, Todd Hester, Piyush Khandelwal, Juhyun Lee, Michael Quinlan, Aibo Tian, Peter Stone, and Mohan Sridharan, “Austin Villa 2010 Standard Platform Team Report,” The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, UT-AI-TR-11-01, 2011.
    [Bibtex]
    @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.",
    }

2010

  • Piyush Khandelwal, Matthew Hausknecht, Juhyun Lee, Aibo Tian, and Peter Stone, “Vision Calibration and Processing on a Humanoid Soccer Robot,” in The Fifth Workshop on Humanoid Soccer Robots at International Conference on Humanoid Robots, 2010.
    [Bibtex]
    @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 at International Conference on Humanoid Robots",
    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",
    }