Julien Diard

Bayesian Robot Programming

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.

Programmation bayésienne des robots

Cet article propose une mthode originale de programmation des robots fonde sur linfrence et lapprentissage baysien. Cette mthode traite formellement des problmes dincertitude et dincompltude inhrents au domaine considr. La principale difficult de la programmation des robots vient de linvitable incompltude des modles utiliss. Nous exposons le formalisme de description dune tche robotique ainsi que les mthodes de rsolution. Nous lillustrons en utilisant ce systme pour programmer une application de surveillance pour un robot mobile : le Khepera. Pour cela, nous utilisons des ressources gnriques de programmation appeles descriptions . Nous montrons comment dfinir et utiliser de manire incrmentale ces ressources (comportements ractifs, fusion capteur, reconnaissance de situations et squences de comportements) dans un cadre systmatique et unifi

Learning Bayesian models of sensorimotor interaction: from random exploration toward the discovery of new behaviors

We are interested in probabilistic models of space and navigation. We describe an experiment where a Koala robot uses experimental data, gathered by randomly exploring the sensorimotor space, so as to learn a model of its interaction with the environment. This model is then used to generate a variety of new behaviors, from obstacle avoidance to wall following to ball pushing, which were previously unknown by the robot. The learned model can be seen as a building block for a hierarchical control architecture based on the Bayesian map formalism.

Merging probabilistic models of navigation: the Bayesian map and the superposition operator

This paper deals with the probabilistic modeling of space, in the context of mobile robot navigation. We define a formalism called the Bayesian map, which allows incremental building of models, thanks to the superposition operator, which is a formally well-defined operator. Firstly, we present a syntactic version of this operator, and secondly, a version where the previously obtained model is enriched by experimental learning. In the resulting map, locations are the conjunction of underlying possible locations, which allows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios.

Bayesian Modeling and Reasoning for Real World Robotics: Basics and Examples

Cognition and Reasoning with uncertain and partial knowledge is a challenge for autonomous mobile robotics. Previous robotics systems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases the possibility for robotics systems to increase their quality of perception on their environment and helps them to take the right actions towards a more realistic and robust behavior. Dealing with uncertainty is thus a major challenge for robotics in a real and unconstrained environment. Here, we propose a new formalism and methodology called Bayesian Programming which aims at the design of efficient robotics systems evolving in a real and uncontrolled environment. The formalism will be exemplified and validated by two interesting experiments.