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Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

International audience; This work revisits the stochastic computationparadigm as a way to implement architectures dedicated toBayesian computation. It is assumed that Stochastic BayesianMachines (SMBs) are intrinsically tolerant to the effects ofradiation. However, practical assessment is mandatory beforeconsidering SBMs in hazardous environments. Results of faultinjectioncampaigns performed at the RTL level provide the firstevidences of SBMs robustness with respect to SEUs and SETs.

International audience; In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform Bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.

We present a unifying framework for exact and approximate inference in Bayesian networks. This framework is used in "ProBT", a general purpose inference engine for probabilistic reasoning and incremental model construction. This paper is not intended to present ProB T but to describe its underlying algorithms mainly the "Successive Restrictions Algorithm " (SRA) for exact inference, and the "Monte Carlo Simultaneous Estimation and Maximization" (MCSEM) algorithm for approximate inference problems. The main idea of ProBT is to use "probability expressions " that can be "exact" or "approximate" as basic bricks to build more complex models incrementally.

The work presented in this paper is part of the BAMBI project, which aims at better understanding natural cognition by designing non Von Neumann machines with biologicaly plausible hardware. Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. Our work on the automatic design of probabilistic machines computing soft inferences with an arithmetic based on stochastic bitstreams allowed us to develop the following compilation toolchain: given a high level description of some general problem (typically to infer some knowledge from a model given some observations), formalized as a Bayesian Program, our toolchain automatically builds a low level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic.We designed as a validating example a circuit description of a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications.

International audience; We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. To solve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler.

Fighting corruption connected with public procurement and governmental agencies requires a strong and effective audit function. The scale and the complexity of the roles to be considered can prevent the use of most audit methods and technologies so successful on the corporate world. The aim of this chapter is to propose a data mining method, based on naive Bayes classifiers, to support a generic risk assessment process for audit planning. The method can sort auditable units by total risk score, fostering dedicated audit coverage to high-risk areas. Audit organizations can transition from a reactive response to a proactive approach to identify and correct issues that may be indicative of fraud, waste or abuse. Extensive databases containing records from government operations can be combined to auditors knowledge, fraud profiles, impact factors or any other relevant metric in order to rank an audit universe.

This work is within the context of Computer-assisted Orthopaedic Surgery (CAOS), in particular Total Hip Replacement (THR) surgery.

This paper deals with the problem of planning safe trajectories for a manipulator robot moving in an environment including both static obstacles and objects (other robots, conveyors ...) whose positions may change during the interval of time separating two planning steps. In order to solve this problem for a general manipulator having six degrees of freedom, we have developed and implemented a method combining two different path planners: a local planner whose purpose is to take the manipulator away from the constrained initial and final configurations, and a global planner which is in charge of generating a safe trajectory for the first three degrees of freedom of the arm. Both the local and the global planners are based on a fast iterative algorithm for computing the distance between pairs of objects. These planners have been implemented within a robot programming system including a solid modeler dedicated to robotics applications and a connection to a robot controller.

We describe a robot system capable of locating a part in an unstructured pile of objects, choose a grasp on the part, plan a motion to reach the part safely, and plan a motion to place the part at a commanded position. The system requires as input a polyhedral world model including models of the part to be manipulated, the robot arm, and any other fixed objects in the environment. In addition, the system builds a depth map, using structured light, of the area where the part is to be found initially. Any other objects present in that area do not have to be modeled.

voir basilic : http://emotion.inrialpes.fr/bibemotion/2005/DBM05/; International audience; 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.

Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Collision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these examples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at http://www-laplace.imag.fr.

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/DBM04/ address: New Orleans, LA (US); This paper presents a new method for probabilistic modeling of space, called the Bayesian Map formalism. It offers a generalization of some common approaches found in the literature, as it does not constrain the dependency structure of the probabilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction operator. In the resulting hierarchy, localization in the high level model is based on probabilistic competition of the lower level models. Experimental results validate the concept, and hint at its usefulness for large scale scenarios.

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/DBM04a/ address: New Orleans, LA (US); This paper deals with the domain of space modeling for mobile robotics. It offers a comparison of probabilistic and biomimetic models of navigation. Both approaches are shown to be quite complementary: while the probabilistic methods exploit sound theoretical grounds, they lack the modularity and, as a consequence, flexibility, of their biomimetic counterparts. We propose a new formalism, called the Bayesian Map formalism, that attempts to bridge the gap between the two domains: it is based on Bayesian modeling and inference for defining the building blocks, and uses operators for building hierarchies of models.

International audience; 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 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.

International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.

The authors address the problem of controlling a remote arm when a large communication delay exists between the control room and the remote workcell. To minimize the volume of information transiting through the communication link and to avoid the problems due to the delay of transmission, the system makes use of two identical world models of the remote workcell: one installed physically close to the operator and the other installed within the controller of the remote site. The communication link only transmits task level robot commands or goals to be achieved. Both sites are equipped with two sets of identical planners, one set is used to test if the generated task level commands can be processed by these planners and the other to plan and execute the actual commands on the remote workcell. After an overview of existing teleprogramming environments, the authors present a general description of the system. Finally they describe the experimental testbed and the results obtained with their method.

The repair of nuclear power plants often necessitates the development of special robots which should be operational readily. This requires a software architecture which quickly permits the design of a controller for an entire telerobotic system in a minimum period of time. We have developed such a software architecture to facilitate the design of applications dealing with nonstandard robot systems and complex industrial processes. In this paper we describe the requirements for this architecture and how we address them.

A computational approach to direct and generalized inverse model acquisition is presented. The approach is based on a proposed method to direct model acquisition from partial information. The method decomposes a hyper-space function in one variable functions, simplifying the learning problem. The acquired direct model is then implemented in a tree-like structure that can be used in the inverse sense without additional learning effort. The authors' approach is able to acquire complete models in hyper-spaces requiring only selected data focused in one-dimension sub-spaces, strongly reducing the data acquisition effort. The authors' approach is particularly interesting for applications in robotics. The acquisition of direct models in robotics frequently takes place in high dimension phase spaces. When traditional approximation methods are used, enormous data bases, containing the examples to be interpolated, are required.

This paper presents a very simple robotic experiment to illustrate how probabilistic reasoning may be used for sensory-motor systems. We will show how our robot may learn internal representations of its interactions with the environment, how it may predict the sensory result of a given action, how it may generate motor command to reach a wished sensory situation, how it may recognize different status and novel conditions and finally how it may behave consistently to explore and exploit simultaneously its environment.

This paper deals with the problem of planning safe trajectories for a manipulator robot moving in an environment including both static obstacles and objects (other robots, conveyors) whose positions may change during the interval of time separating two planning steps. In order to solve this problem for a general manipulator having six degrees of freedom, the authors have developed and implemented a method combining two different path planners: a local planner whose purpose is to take the manipulator away from the constrained initial and final configurations, and a global planner which is in charge of generating a safe trajectory for the first three degrees of freedom of the arm. Both the local and the global planners are based on a fast iterative algorithm for computing the distance between pairs of objects. These planners have been implemented within a robot programming system (called ACT) including a solid modeler dedicated to robotics applications and a connection to a robot controller. >

International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.

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” [1]. 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.