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Maximum Likelihood Estimation of Sensor and Action Model Functions on a Mobile Robot.
Daniel
Stronger and Peter Stone.
In IEEE International Conference on Robotics
and Automation, May 2008.
ICRA 2008
[PDF]182.1kB [postscript]902.7kB
In order for a mobile robot to accurately interpret its sensations and predict the effects of its actions, it must have accurate models of its sensors and actuators. These models are typically tuned manually, a brittle and laborious process. Autonomous model learning is a promising alternative to manual calibration, but previous work has assumed the presence of an accurate action or sensor model in order to train the other model. This paper presents an adaptation of the Expectation-Maximization (EM) algorithm to enable a mobile robot to learn both its action and sensor model functions, starting without an accurate version of either. The resulting algorithm is validated experimentally both on a Sony Aibo ERS-7 robot and in simulation.
@InProceedings{ICRA08-stronger,
author="Daniel Stronger and Peter Stone",
title="Maximum Likelihood Estimation of Sensor and Action Model Functions on a Mobile Robot",
booktitle="{IEEE} International Conference on Robotics and Automation",
location="Pasadena, CA",
month="May",
year="2008",
abstract="In order for a mobile robot to accurately interpret its sensations and
predict the effects of its actions, it must have accurate models of
its sensors and actuators. These models are typically tuned manually,
a brittle and laborious process. Autonomous model learning is a
promising alternative to manual calibration, but previous work has
assumed the presence of an accurate action or sensor model in order to
train the other model. This paper presents an adaptation of the
Expectation-Maximization (EM) algorithm to enable a mobile robot to
learn both its action and sensor model functions, starting without an
accurate version of either. The resulting algorithm is validated
experimentally both on a Sony Aibo ERS-7 robot and in simulation.",
wwwnote={<a href="http://www.icra2008.org/">ICRA 2008</a>},
}
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