Nate Kohl's Publications

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From Pixels to Multi-Robot Decision-Making: A Study in Uncertainty

From Pixels to Multi-Robot Decision-Making: A Study in Uncertainty. Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, and Nicholas K. Jong. Robotics and Autonomous Systems, 54:933–943, 2006. Special issue on Planning Under Uncertainty in Robotics.
Contains material that was previously published in an ICRA-2006 paper. Online Version

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Abstract

Mobile robots must cope with uncertainty from many sources along the path from interpreting raw sensor inputs to behavior selection to execution of the resulting primitive actions. This article identifies several such sources and introduces methods for i) reducing uncertainty and ii) making decisions in the face of uncertainty. We present a complete vision-based robotic system that includes several algorithms for learning models that are useful and necessary for planning, and then place particular emphasis on the planning and decision-making capabilities of the robot. Specifically, we present models for autonomous color calibration, autonomous sensor and actuator modeling, and an adaptation of particle filtering for improved localization on legged robots. These contributions enable effective planning under uncertainty for robots engaged in goal-oriented behavior within a dynamic, collaborative and adversarial environment. Each of our algorithms is fully implemented and tested on a commercial off-the-shelf vision-based quadruped robot.

BibTeX Entry

@Article{stone:ras06,
  author = "Peter Stone and Mohan Sridharan and Daniel Stronger and Gregory Kuhlmann and Nate Kohl and Peggy Fidelman and Nicholas K.\ Jong",
  title = "From Pixels to Multi-Robot Decision-Making:  A Study in Uncertainty",
  journal = "Robotics and Autonomous Systems",
  volume = "54",
  issue = "11",
  year = "2006",
  pages = "933--943",
  note = "Special issue on Planning Under Uncertainty in Robotics.",
  abstract = {
          Mobile robots must cope with uncertainty from many sources
          along the path from interpreting raw sensor inputs to
          behavior selection to execution of the resulting primitive
          actions.  This article identifies several such sources and
          introduces methods for i) reducing uncertainty and ii)
          making decisions in the face of uncertainty.  We present a
          complete vision-based robotic system that includes several
          algorithms for learning models that are useful and necessary
          for planning, and then place particular emphasis on the
          planning and decision-making capabilities of the robot.
          Specifically, we present models for autonomous color
          calibration, autonomous sensor and actuator modeling, and an
          adaptation of particle filtering for improved localization
          on legged robots.  These contributions enable effective
          planning under uncertainty for robots engaged in
          goal-oriented behavior within a dynamic, collaborative and
          adversarial environment.  Each of our algorithms is fully
          implemented and tested on a commercial off-the-shelf
          vision-based quadruped robot.},
  wwwnote = {Contains material that was previously published in an <a href="http://www.cs.utexas.edu/~pstone/Papers/2005icra/actsense.pdf">ICRA-2006 paper</a>.  <a href="http://dx.doi.org/10.1016/j.robot.2006.05.010">Online Version</a>},
  bib2html_pubtype = {Journal},
  bib2html_rescat = {Robot Soccer, Robotics, Machine Learning}
}

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