---
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.'
altloc:
  - http://www-leibniz.imag.fr/LAPLACE/Publications/Rayons/Lebeltel2000.pdf
chapter: ~
commentary: ~
commref: ~
confdates: ~
conference: ~
confloc: ~
contact_email: ~
creators_id: []
creators_name:
  - family: Lebeltel
    given: Olivier
    honourific: ''
    lineage: ''
  - family: Bessiere
    given: Pierre
    honourific: ''
    lineage: ''
  - family: Diard
    given: Julien
    honourific: ''
    lineage: ''
  - family: Mazer
    given: Emmanuel
    honourific: ''
    lineage: ''
date: 2000
date_type: published
datestamp: 2001-07-05
department: Laboratoire LEIBNIZ
dir: disk0/00/00/16/70
edit_lock_since: ~
edit_lock_until: ~
edit_lock_user: ~
editors_id: []
editors_name: []
eprint_status: archive
eprintid: 1670
fileinfo: /style/images/fileicons/application_postscript.png;/1670/1/Lebeltel2000.ps|/style/images/fileicons/application_pdf.png;/1670/5/Lebeltel2000.pdf
full_text_status: public
importid: ~
institution: CNRS
isbn: ~
ispublished: pub
issn: ~
item_issues_comment: []
item_issues_count: 0
item_issues_description: []
item_issues_id: []
item_issues_reported_by: []
item_issues_resolved_by: []
item_issues_status: []
item_issues_timestamp: []
item_issues_type: []
keywords: Robotics Bayes Pereception Inference Action
lastmod: 2011-03-11 08:54:44
latitude: ~
longitude: ~
metadata_visibility: show
note: ~
number: ~
pagerange: ~
pubdom: TRUE
publication: ~
publisher: ~
refereed: FALSE
referencetext: |
  Alami et al., 1998
  Alami, R., Chatila, R., Fleury, S., Ghallab, M. & Ingrand, F. ; (1998) ; An Architecture for Autonomy ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 315-337
  Aycard, 1998
  Aycard, O.; (1998) ; Architecture de contrôle pour robot mobile en environnement intérieur structuré ; Ph.D. thesis, Univeristé Henri Poincaré, Nancy, France
  Bernhardt & Albright, 1993
  Bernhardt, R. & Albright, S.L. (editors) ; (1993) ; Robot Calibration ; Chapman & Hall
  Bessière et al., 1998a
  Bessière, P., Dedieu, E., Lebeltel, O., Mazer, E. & Mekhnacha, K. ; (1998a) ; Interprétation ou Description (I) : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs ; Intellectica ; Vol. 26-27, pp. 257-311; Paris, France
  Bessière et al., 1998b
  Bessière, P., Dedieu, E., Lebeltel, O., Mazer, E. & Mekhnacha, K. ; (1998a) ; Interprétation ou Description (I) : Fondements mathématiques de l’approche F+D ; Intellectica ; Vol. 26-27, pp. 313-336 ; Paris, France
  Borrelly et al., 1998
  Borrelly, J-J., Coste, E., Espiau, B., Kapellos, K., Pissard-Gibollet, R., Simon, D. & Turro, N.; (1998) ; The ORCCAD Architecture ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 338-359
  Brafman et al., 1997
  Brafman, R.I., Latombe, J-C., Moses, Y. & Shoham, Y.; (1997) ; Applications of a logic of knowledge to motion planning under uncertainty ; Journal of the ACM, vol.44(5), pp. 633-68
  Bretthorst, 1988
  Bretthorst, G.L. ; (1988) ; Bayesian spectrum analysis and parameter estimation ; Spinger Verlag
  Brooks, 1986
  Brooks, R.A. ; (1986) ; A robust layered control systems for a mobile robot ; IEEE Journal of Robotics and Automation ; Vol. 2(1), pp. 14-23
  Cooper, 1990
  Cooper, G. ; (1990) ; The computational complexity of probabilistic inference using Bayesian belief networks ; Artificial Intelligence, Vol. 42, pp. 393-405
  Cox, 1961
  Cox, R.T. ; (1961) ; The algebra of probable inference ; The John Hopkins Press, Baltimore, USA
  Cox, 1979
  Cox, R.T. ; (1979) ; Of inference and inquiry, an essay in inductive logic ; in The maximum entropy formalism, edited by Raphael D. Levine & Myron Tribus ; M.I.T. Press, U.S.A.
  Dagum & Luby, 1993
  Dagum, P. & Luby, M. ; (1993) ; Approximate probabilistic reasoning in Bayesian belief network is NP-Hard ; Artificial Intelligence, Vol. 60, pp. 141-153
  Darwiche & Provan, 1997
  Darwiche, A. and Provan, G. ; (1997) ; Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference ; Journal of Artificial Intelligence Research (JAIR), Vol. 6, pp. 147-176
  Dedieu, 1995
  Dedieu, E. ; (1995) ; La représentation contingente : Vers une reconciliation des approches fonctionnelles et structurelles de la robotique autonome. Thèse de troisième cycle INPG (Institut National Polytechnique de Grenoble) ; Grenoble, France
  Dekhil & Henderson, 1998
  Dekhil, M. & Henderson, T.C. ; (1998) ; Instrumented Sensor System Architecture ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 402-417
  Delcher et al., 1996
  Delcher, A.L., Grove, A.J., Kasif, S. and Pearl, J. ; (1996) ; Logarithmic-Time Updates and Queries in Probabilistic Networks ; Journal of Artificial Intelligence Research (JAIR) ; Vol. 4, pp. 37-59
  Diard & Lebeltel, 1999
  Diard, J. & Lebeltel, O. ; (1999) ; Bayesian Learning Experiments with a Khepera Robot in Experiments with the Mini-Robot Khepera : Proceedings of the 1st International Khepera Workshop, December 1999, Löffler Mondada Rückert (Editors), Paderborn, HNI-Verlagsschriftenreihe ; Band 64 ; Germany ; pp. 129-138 ; 
  Donald, 1988
  Donald, B.R.; (1988) ; A geometric approach to error detection and recovery for robot motion planning with uncertainty ; Artificial Intelligence, vol.37, pp. 223-271
  Erickson & Smith, 1988a
  Erickson, G.J. & Smith, C.R. ; (1988a) ; Maximum-Entropy and Bayesian methods in science and engineering ; Volume 1 : Foundations ; Kluwer Academic Publishers
  Erickson & Smith, 1988b
  Erickson, G.J. & Smith, C.R. ; (1988b) ; Maximum-Entropy and Bayesian methods in science and engineering ; Volume 2 : Applications ; Kluwer Academic Publishers
  Frey, 1998
  Frey, B.J. ; (1998) ; Graphical Models for Machine Learning and Digital Communication ; MIT Press
  Halpern, 1999a
  Halpern, J.Y. ; (1999a) ; A Counterexample to Theorems of Cox and Fine ; Journal of Artificial Intelligence Research (JAIR), Vol. 10, pp. 67-85.
  Halpern, 1999b
  Halpern, J.Y. ; (1999b) ; Cox's Theorem Revisited ; Journal of Artificial Intelligence Research (JAIR), Vol. 11, pp. 429-435.
  Jaakola & Jordan, 1999
  Jaakkola, T.S. and Jordan, M.I. ; (1999) ; Variational Probabilistic Inference and the QMR-DT Network ; Journal of Artificial Intelligence Research (JAIR), Vol. 10, pp. 291-322
  Jaynes, 1979
  Jaynes, E.T. ; (1979) ; Where do we Stand on Maximum Entropy? ; in The maximum entropy formalism ; edited by Raphael D. Levine & Myron Tribus ; M.I.T. Press
  Jaynes, 1982
  Jaynes, E.T. ; (1982) ; On the rationale of maximum-entropy methods ; Proceedings of the IEEE
  Jaynes, 1998
  Jaynes, E.T. ; (1998) ; Probability theory - The logic of science ; unfinished book available at http://bayes.wustl.edu/etj/prob.html
  Jordan & Jacobs, 1994
  Jordan MI and Jacobs RA (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation ; Vol. 6, pp. 181-214.
  Jordan, 1998
  Jordan, M. ; (1998) ; Learning in Graphical Models ; MIT Press
  Jordan et al., 1999
  Jordan, M., Ghahramani, Z., Jaakkola, T.S. & Saul, L.K. ; (1999) ; An introduction to variational methods for graphical models ; In press, Machine Learning
  Kaelbling, Littman & Cassandra, 1996
  Kaelbling, L.P., Littman, M.L. & Cassandra, A.R.; (1996) ; Partially observable Markov decision processes for artificial intelligence ; Reasoning with Uncertainty in Robotics. International Workshop, RUR'95, Proceedings pp.146-62 ; Springer-Verlag
  Kapur & Kesavan, 1992
  Kapur, J.N., & Kesavan, H.K. ; (1992) ; Entropy optimization principles with applications ; Academic Press
  Laplace, 1774
  Laplace, Pierre Simon de (1774); Mémoire sur la probabilités des causes par les évènements; Mémoire de l’académie royale des sciences; Reprinted in Oeuvres complètes de Laplace, (vol. 8), Gauthier Villars, Paris, France
  Laplace, 1814
  Laplace, Pierre Simon de (1814); Essai philosphique sur les probabilités; Courcier Imprimeur, Paris; Reprinted in Oeuvres complètes de Laplace, (vol. 7), Gauthier Villars, Paris, France
  Lauritzen & Spiegehalter, 1988
  Lauritzen, S. & Spiegelhalter, D. ; (1988) ; Local computations with probabilities on graphical structures and their application to expert systems ; Journal of the Royal Stastical Society B ; Vol. 50, pp. 157-224
  Lauritzen, 1996
  Lauritzen, S. L. ; (1996) ; Graphical Models ; Oxford University Press
  Lebetel, 1999
  Lebeltel, O. ; (1999) ; Programmation Bayésienne des Robots ; Ph.D. Thesis, Institut National Polytechnique de Grenoble (INPG); Grenoble, France
  Lozano-Perez et al., 1984
  Lozano-Perez, T., Mason, M.T., Taylor, R.H.; (1984) ; Automatic synthesis of fine-motion strategies for robots ; International Journal of Robotics Research, vol.3(1), pp. 3-24
  Maes, 1989
  Maes, P. ; (1989) ; How to Do the Right Thing ; Connection Science Journal ; Vol. 1, N°3, pp. 291-323
  Matalon, 1967
  Matalon, B. ; (1967) ; Epistémologie des probabilités ; in Logique et connaissance scientifique edited by Jean Piaget ; Encyclopédie de la Pléiade ; Editions Gallimard ; Paris, France
  Mazer et al., 1998
  Mazer, E., Boismain, G., Bonnet des Tuves, J., Douillard, Y., Geoffroy, S., Dubourdieu, J., Tounsi, M. & Verdot, F.; (1998) ; START: an Industrial System for Teleoperation, Proc. of the IEEE Int. Conf. on Robotics and Automation, Vol. 2, pp. 1154-1159, Leuven (BE)
  Mekhnacha, 1999
  Mekhnacha, K. ; (1999) ; Méthodes probabilistes baysiennes pour la prise en compte des incertitudes géométriques : Application à la CAO-robotique ; Ph.D. thesis INPG (Institut National Polytechnique de Grenoble), Grenoble, France
  Mohammad-Djafari & Demoment, 1992
  Mohammad-Djafari, A.& Demoment, G. ; (1992) ; Maximum entropy and bayesian methods ; Kluwer Academic Publishers
  Pearl, 1988
  Pearl, J. ; (1988) ; Probabilistic reasoning in intelligent systems : Networks of plausible inference ; Morgan Kaufmann Publishers ; San Mateo, California, USA
  Robert, 1990
  Robert, C. ; (1990) ; An entropy concentration theorem: applications ; in artificial intelligence and descriptive statistics ; Journal of Applied Probabilities
  Robinson, 1965
  Robinson, J.A. ; (1965) ; A Machine Oriented Logic Based on the Resolution Principle ; Jour. Assoc. Comput. Mach.; vol. 12
  Robinson, 1979
  Robinson, J.A. ; (1979) ; Logic : Form and Function ; North-Holland, New York, USA
  Robinson & Sibert, 1983a
  Robinson, J.A. & Sibert, E.E. ; (1983a) ; LOGLISP : an alternative to PROLOG ; Machine Intelligence, Vol. 10.
  Robinson & Sibert, 1983b
  Robinson, J.A. & Sibert, E.E. ; (1983b) ; LOGLISP : Motivation, design and implementation ; Machine Intelligence, Vol. 10.
  Ruiz et al., 1998
  Ruiz, A., Lopez-de-Teruel, P.E. and Garrido, M.C. ; (1998) ; Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians ; Journal of Artificial Intelligence Research (JAIR) ; Vol. 9, pp. 167-217
  Saul et al., 1996
  Saul, L.K., Jaakkola, T. and Jordan, M.I. ; (1996) ; Mean Field Theory for Sigmoid Belief Networks ; Journal of Artificial Intelligence Research (JAIR), Vol. 4, pp. 61-76
  Schneider et al., 1998
  Schneider, S.A., Chen, V.W., Pardo-Castellote, G., Wang, H.H.; (1998) ; ControlShell: A Software Architecture for Complex Electromechanical Systems ; International Journal for Robotics Research (IJRR) ; Vol. 17(4), pp. 360-380
  Smith & Grandy, 1985
  Smith, C.R. & Grandy, W.T. Jr. ; (1985) ; Maximum-Entropy and bayesian methods in inverse problems ; D. Reidel Publishing Company
  Tarentola, 1987
  Tarentola, A. ; (1987) ; Inverse Problem Theory: Methods for data fitting and model parameters estimation ; Elsevier ; New York, USA
  Thrun, 1998
  Thrun, S.; (1998) ; Bayesian landmark learning for mobile robot localization ; Machine Learning, vol. 33(1), pp.41-76
  Zhang & Poole, 1996
  Zhang, N.L. and Poole, D. ; (1996) ; Exploiting Causal Independence in Bayesian Network Inference ; Journal of Artificial Intelligence Research (JAIR), Vol. 5, pp. 301-328
relation_type: []
relation_uri: []
reportno: ~
rev_number: 14
series: ~
source: ~
status_changed: 2007-09-12 16:39:32
subjects:
  - comp-sci-art-intel
  - comp-sci-robot
  - comp-sci-stat-model
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Bayesian robot Programming
type: techreport
userid: 129
volume: ~