Bayesian Robot Programming

International audienceWe 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 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 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

Motion Strategies for Visibility based Target Tracking in Unknown Environments

Ph.DDOCTOR OF PHILOSOPH

Multi-robot cooperative surveillance in unknown environments

Ph.DDOCTOR OF PHILOSOPH

Chasing an Elusive Target With a Mobile Robot

International audienceThis paper describes how a mobile robot (a six-wheeled Koala equiped with a PAL pan-tilt camera) can chase an elusive target (a remote controlled toy car) in an unknown and unconstrained environment. The first purpose of the paper is to demonstrate efficiency, simplicity, and adequacy of Bayesian Robot Programming (BRP) to quickly develop such applications. The second purpose of the paper is to illustrate that tremendous information compression ratio may be obtained by some pertinent sensori-motor decoupling