Formal and Informal Methods for Multi-Core Design Space Exploration

We propose a tool-supported methodology for design-space exploration for embedded systems. It provides means to define high-level models of applications and multi-processor architectures and evaluate the performance of different deployment (mapping, scheduling) strategies while taking uncertainty into account. We argue that this extension of the scope of formal verification is important for the viability of the domain.Comment: In Proceedings QAPL 2014, arXiv:1406.156

Bayesian robot Programming

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

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

LNCS

We introduce in this paper AMT 2.0 , a tool for qualitative and quantitative analysis of hybrid continuous and Boolean signals that combine numerical values and discrete events. The evaluation of the signals is based on rich temporal specifications expressed in extended Signal Temporal Logic (xSTL), which integrates Timed Regular Expressions (TRE) within Signal Temporal Logic (STL). The tool features qualitative monitoring (property satisfaction checking), trace diagnostics for explaining and justifying property violations and specification-driven measurement of quantitative features of the signal

Programmation Bayésienne des Robots

This thesis proposes an original method for robotic programming 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 the probability calculus, suggested by the physicist E T Jaynes: "Probability as Logic". Learning and maximum entropy principle translates incompleteness into uncertainty. The main contribution of this thesis 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 an unified framework. We discuss the various advantages of our approach: statement of preliminary knowledge, taking into account uncertainty, direct and inverse programming. We suggest perspectives for our work: choice of architecture and planning. We place our work within a wider epistemological horizon while opposing, within the framework of autonomous robotics, the "traditional" approach concerning "high level cognition" and the "reactive" approach associated with the "low level cognition". We finally show how our work proposes to establish a link between these two extremes.Cette thèse propose une méthode originale de programmation de robot fondée sur l'inférence et l'apprentissage bayésien. Cette méthode traite formellement des problèmes d'incertitude et d'incomplétude inhérents au domaine considéré. En effet, la principale difficulté de la programmation des robots vient de l'inévitable incomplétude des modèles utilisés. Nous exposons le formalisme de description d'une tâche robotique ainsi que les méthodes de résolutions. Ce formalisme est inspiré de la théorie du calcul des probabilités, proposée par le physicien E.T. Jaynes : "Probability as Logic". L'apprentissage et les techniques de maximum d'entropie traduisent l'incomplétude en incertitude. L'inférence bayésienne offre un cadre formel permettant de raisonner avec cette incertitude. L'apport principal de cette thèse est la définition d'un système générique de programmation pour la robotique et son application expérimentale. Nous l'illustrons en utilisant ce système pour programmer une application de surveillance pour un robot mobile : le Khepera. Pour cela, nous utilisons des ressources génériques de programmation appelées "descriptions". Nous montrons comment définir et utiliser de manière incrémentale ces ressources (comportements réactifs, fusion capteur, reconnaissance de situations et séquences de comportements) dans un cadre systématique et unifié. Nous discutons des différents avantages de notre approche : expression des connaissances préalables, prise en compte et restitution de l'incertitude, programmation directe et inverse. Nous proposons des perspectives à ce travail : choix d'architecture et planification. Nous situons notre travail dans un cadre épistémologique plus vaste en opposant, dans le cadre de la robotique autonome, l'approche "classique" relevant de la "cognition de haut niveau" et l'approche "réactive" associée à une "cognition de bas niveau". Nous montrons finalement comment nos travaux proposent de faire le lien entre ces deux extrêmes

Bayesian Programming and Hierarchical Learning in Robotics

This paper presents a new robotic programming environment based on the probability calculus. We show how reactive behaviours, like obstacle avoidance, contour following, or even light following, can be programmed and learned by a Khepera robot with our system. We further demonstrate that behaviours can be combined either by programmation or learning. A homing behaviour is thus obtained by combining obstacle avoidance and light following