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Peggy Fidelman and Peter Stone.
Layered Learning on a Physical Robot. In Under Review. , February 2005.
Some videos
of the robot referenced in the paper.
[PDF]151.8kB [postscript]2.5MB
Layered learning is a general hierarchical machine learning paradigm that leverages a given task decomposition to learn complex tasks efficiently. Though it has been validated previously in simulation, this paper presents the first application of layered learning on a physical robot. In particular, it enables the learning of a high-level grasping behavior that relies on a gait which itself must be learned. All learning is done autonomously onboard a commercially available Sony Aibo robot, with no human intervention other than battery changes. We demonstrate that our approach makes it possible to quickly learn both a fast gait and a reliable grasping behavior which, in combination, significantly outperform our best hand-tuned solution.
@InProceedings(IROS2005-chinpinch,
author="Peggy Fidelman and Peter Stone",
title="Layered Learning on a Physical Robot",
booktitle="Under Review. ",
month="February",year="2005",
abstract={
Layered learning is a general hierarchical machine
learning paradigm that leverages a given task
decomposition to learn complex tasks efficiently.
Though it has been validated previously in
simulation, this paper presents the first
application of layered learning on a physical robot.
In particular, it enables the learning of a
high-level grasping behavior that relies on a gait
which itself must be learned. All learning is done
autonomously onboard a commercially available Sony
Aibo robot, with no human intervention other than
battery changes. We demonstrate that our approach
makes it possible to quickly learn both a fast gait
and a reliable grasping behavior which, in
combination, significantly outperform our best
hand-tuned solution.
},
wwwnote={Some <a href="http://www.cs.utexas.edu/users/AustinVilla/?p=research/learned_acquisition">videos of the robot</a> referenced in the paper.},
)
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