CORAL Research Publications

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Acquiring Observation Models through Reverse Plan Monitoring

Sonia Chernova, Elisabeth Crawford and Manuela Veloso. Acquiring Observation Models through Reverse Plan Monitoring. In 12th Portuguese Conference on Artificial Intelligence, Covilh\ a, Portugal, December 2005.

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Abstract

We present a general-purpose framework for updating a robot'sobservation model within the context of planning and execution.Traditional plan execution relies on monitoring plan step transitionsthrough accurate state observations obtained from sensory data. In order to gather meaningful state data from sensors, tediousand time-consuming calibration methods are often required. To addressthis problem we introduce Reverse Monitoring, a process oflearning an observation model through the use of plans composed ofscripted actions. The automatically acquired observation models allow therobot to adapt to changes in the environment and robustly executearbitrary plans. We have fully implemented the method in our AIBOrobots, and our empirical results demonstrate its effectiveness.

BibTeX Entry

@inproceedings{Chernova05epia,
  title="Acquiring Observation Models through Reverse Plan Monitoring",
  author="Sonia Chernova, Elisabeth Crawford and Manuela Veloso",
  booktitle="12th Portuguese Conference on Artificial Intelligence",
  place="Covilh\~a, Portugal", month="December",
  year="2005",
  abstract={We present a general-purpose framework for updating a robot's
observation model within the context of planning and execution.
Traditional plan execution relies on monitoring plan step transitions
through accurate state observations obtained from sensory data.
 In order to gather meaningful state data from sensors, tedious
and time-consuming calibration methods are often required. To address
this problem we introduce \textit{Reverse Monitoring}, a process of
learning an observation model through the use of plans composed of
scripted actions. The automatically acquired observation models allow the
robot to adapt to changes in the environment and robustly execute
arbitrary plans.  We have fully implemented the method in our AIBO
robots, and our empirical results demonstrate its effectiveness.},
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {RoboCup Publications, Robot Learning}
  bib2html_dl_pdf = {http://www.cs.cmu.edu/~soniac/files/ChernovaCrawfordVelosoEPIA06.pdf},
}

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