Bayesian Robot Programming

[bessiere]

We are pleased to announce the publication of the following paper in the
January 2004 issue of "Autonomous Robots" a Kluewer publication:

Bayesian Robot Programming
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Freely available at the following URL:
http://www.kluweronline.com/issn/0929-5593/contents


Abstract:
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We propose a new method to program robots based on Bayesian inference and
learning. It is called BRP for Bayesian Robot Programming. The capacities of
this programming method are demonstrated through a succession of
increasingly complex experiments. Starting from the learning of simple
reactive behaviors, we present instances of behavior combination, sensor
fusion, hierarchical behavior composition, situation recognition and
temporal sequencing. This series of experiments comprises the steps in the
incremental development of a complex robot program. The advantages and
drawbacks of BRP are discussed along with these different experiments and
summed up as a conclusion. These different robotics programs may be seen as
an illustration of probabilistic programming applicable whenever one must
deal with problems based on uncertain or incomplete knowledge. The scope of
possible applications is obviously much broader than robotics.

Keywords:
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Bayesian robot programming, control of autonomous robots, computational
architecture for autonomous systems, theory of autonomous systems

Authors:
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Olivier Lebeltel, Pierre Bessière, Julien Diard & Emmanuel Mazer

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 Dr Pierre BESSIERE ‹ CNRS
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