A Bayesian framework for robotic programming

Olivier Lebeltel 1, 2 Julien Diard 1, 2 Pierre Bessiere 1, 2 Emmanuel Mazer 1, 2
2 SHARP - Automatic Programming and Decisional Systems in Robotics
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes
Abstract : We propose an original method for programming robots based on Bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as well as the resolution methods. This formalism is inspired by the theory of probability, suggested by the physicist E T Jaynes: "Probability as Logic". Learning and maximum entropy principle translate incompleteness into uncertainty. Bayesian inference offers a formal framework for reasoning with this uncertainty. The main contribution of this paper is the definition of a generic system of robotic programming and its experimental application. We illustrate it by programming a surveillance task with a mobile robot: the Khepera. In order to do this, we use generic programming resources called "descriptions". We show how to define and use these resources in an incremental way (reactive behaviors, sensor fusion, situation recognition and sequences of behaviors) within a systematic and unified framework.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00089153
Contributor : Pierre Bessiere <>
Submitted on : Monday, September 11, 2006 - 9:21:36 AM
Last modification on : Thursday, February 7, 2019 - 4:31:11 PM
Long-term archiving on : Tuesday, September 18, 2012 - 4:35:31 PM

Identifiers

  • HAL Id : hal-00089153, version 1

Collections

IMAG | INRIA | UGA

Citation

Olivier Lebeltel, Julien Diard, Pierre Bessiere, Emmanuel Mazer. A Bayesian framework for robotic programming. Twentieth International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2000), 2000, Paris, France. ⟨hal-00089153⟩

Share

Metrics

Record views

329

Files downloads

126