3 papers about BAYESIAN ROBOTICS

[Pierre Bessiere]

3 papers about Bayesian Robotics are available online (comments welcome) 
:

Bayesian Robots Programming

Abstract: We propose a new method to program robots based on Bayesian
inference and learning. 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 combinations, 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 this approach 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.

PDF: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Lebeltel2000.pdf
PS: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Lebeltel2000.ps
PS.GZ:
http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Lebeltel2000.ps.gz

Reference: Lebeltel O., Bessi=E8re P., Diard J. & Mazer E. (2000); Bayesian
Robots Programming; Les cahiers du Laboratoire Leibniz (Technical Report),
n=B01, Mai 2000; Grenoble, France

The Design and Implementation of a Bayesian CAD Modeler for Robotic
Applications

Abstract: We present a Bayesian CAD modeler for robotic applications. We
address the problem of taking into account the propagation of geometric
uncertainties when solving inverse geometric problems. The proposed method
may be seen as a generalization of constraint-based approaches in which we
explicitly model geometric uncertainties. Using our methodology, a
geometric constraint is expressed as a probability distribution on the
system's parameters and the sensor measurements, instead of a simple
equality or inequality. To solve geometric problems in this framework, we
propose an original resolution method able to adapt to problem complexity.
Using two examples, we show how to apply our approach by providing
simulation results using our modeler.

PDF: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Mekhnacha2000.pdf
PS: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Mekhnacha2000.ps
PS.GZ:
http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Mekhnacha2000.ps.gz

Reference: Mekhnacha K., Mazer E. & Bessi=E8re P. (2000); The Design and
Implementation of a Bayesian CAD Modeler for Robotic Applications; Les
cahiers du Laboratoire Leibniz (Technical report), n=B02, Mai 2000; Grenoble,
France

State Identification for Planetary Rovers: Learning and Recognition

Abstract: A planetary rover must be able to identify states where it should
stop
or change its plan.  With limited and infrequent communication from ground,
the rover must recognize states accurately. However, the sensor data is
inherently noisy, so identifying the
temporal patterns of data that correspond to interesting or important
states becomes a complex problem.  In this paper, we present an approach to
state identification using second-order Hidden Markov Models.  Models are
trained automatically on a set of labeled training data; the rover uses
those models to identify its state from the observed data.  The approach is
demonstrated on data
from a planetary rover platform.

PDF: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Aycard2000.pdf
PS: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Aycard2000.ps
PS.GZ: http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Aycard2000.ps.gz

Reference: O. Aycard and R. Washington.(2000) State Identificationfor
Planetary Rovers: Learning and Recognition. In Proceedings of the 2000 IEEE
International Conference on Robotics and Automation. San Francisco, USA.

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 ___________________________________________________________________
 Dr Pierre BESSIERE                    CNRS
 *********************
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