logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Bayesian Robot Programming

delete2004-01-01
delete80
PRE
AI
O
Olivier Lebeltel
P
Pierre Bessìère
J
Julien Diard
E
Emmanuel Mazer
DOI：10.1023/B:AURO.0000008671.38949.43delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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:
Bayesian robot programming
control of autonomous robots
computational architecture for autonomous systems
theory of autonomous systems
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Autonomous Robots cover
Autonomous Robots
IF:
4.3
Papers:
1.7K
Citations:
5.0K

Organization

No organization information available