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�re P., Diard J. & Mazer E. (2000); Bayesian Robots Programming; Les cahiers du Laboratoire Leibniz (Technical Report), n�1, 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�re P. (2000); The Design and Implementation of a Bayesian CAD Modeler for Robotic Applications; Les cahiers du Laboratoire Leibniz (Technical report), n�2, 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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