Modèles probabilistes formels pour problèmes cognitifs usuels

International audienceHow can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.Comment un modèle incomplet et incertain de l'environnement peut-il être utilisé pour décider, agir, apprendre, raisonner et percevoir efficacement ? Voici le défi central que les systèmes cognitifs tant naturels qu'artificiels doivent résoudre. La logique, de par sa nature même, faite de certitudes et ne laissant aucune place au doute, est incapable de répondre à cette question. L'approche subjectiviste des probabilités est une extension de la logique conçue pour pallier ce manque. Dans cet article, nous passons en revue un ensemble de problèmes cognitifs usuels et nous montrons comment les formuler et les résoudre avec un formalisme probabiliste unique. Les concepts abordés sont : l'ambigüité, la fusion, la multi-modalité, les conflits, la modularité, les hiérarchies et les boucles. Chacune de ces questions est tout d'abord brièvement présentée à partir d'exemples venant des neurosciences, de la psychophysique ou de la robotique. Ensuite, le concept est formalisé en utilisant un modèle générique bayésien. Enfin, les hypothèses, les points communs et les différences de chacun de ces modèles sont analysés et discutés

Common Bayesian Models for Common Cognitive Issues

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discusse

Online Human Activity Recognition for Ergonomics Assessment

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Comparing ICP variants on real-world data sets: Open-source library and experimental protocol

Many modern sensors used for mapping produce 3D point clouds, which are typically registered together using the iterative closest point (ICP) algorithm. Because ICP has many variants whose performances depend on the environment and the sensor, hundreds of variations have been published. However, no comparison frameworks are available, leading to an arduous selection of an appropriate variant for particular experimental conditions. The first contribution of this paper consists of a protocol that allows for a comparison between ICP variants, taking into account a broad range of inputs. The second contribution is an open-source ICP library, which is fast enough to be usable in multiple real-world applications, while being modular enough to ease comparison of multiple solutions. This paper presents two examples of these field applications. The last contribution is the comparison of two baseline ICP variants using data sets that cover a rich variety of environments. Besides demonstrating the need for improved ICP methods for natural, unstructured and information-deprived environments, these baseline variants also provide a solid basis to which novel solutions could be compared. The combination of our protocol, software, and baseline results demonstrate convincingly how open-source software can push forward the research in mapping and navigatio

Online Human Activity Recognition for Ergonomics Assessment

International audienceWe address the problem of recognizing the current activity performed by a human worker, providing an information useful for automatic ergonomic evaluation of workstations for industrial applications.Traditional ergonomic assessment methods rely on pen-and-paper worksheet, such as the Er-gonomic Assessment Worksheet (EAWS). Nowadays, there exists no tool to automatically estimate the ergonomics score from sensors (external cameras or wearable sensors). As the ergonomic evaluation depends of the activity that is being performed, the first step towards a fully automatic ergonomic assessment is to automatically identify the different activities within an industrial task. To address this problem, we propose a method based on wearable sensors and supervised learning based on Hidden Markov Model (HMM). The activity recognition module works in two steps. First, the parameters of the model are learned offline from observation based on both sensors, then in a second stage, the model can be used to recognize the activity offline and online. We apply our method to recognize the current activity of a worker during a series of tasks typical of the manufacturing industry. We recorded 6 participants performing a sequence of tasks using wearable sensors.Two systems were used: the MVN Link suit from Xsens and the e-glove from Emphasis Telematics (See Fig. 1). The first consists of 17 wireless inertial sensors embedded in a lycra suit, and is used to track the whole-body motion. The second is a glove that includes pressure sensors on fingertips, and finger flexion sensors. The motion capture data are combined with the one from the glove and fed to our activity recognition model. The tasks were designed to involve elements of EAWS such as load handling, screwing and manipulating objects while in different static postures. The data are labeled following the EAWS categories such as " standing bent forward " , " overhead work " or " kneeling ". In terms of performances, the model is able to recognize the activities related to EAWS with 91% of precision by using a small subset of features such as the vertical position of the center of mass, the velocity of the center of mass and the angle of the L5S1 joint

Text and Paintings

The manuscript now preserved as Indien 745 in the Manuscript Department of the Bibliothèque nationale de France (BnF) contains 137 paintings by an Indian artist, each accompanied by an explanation in French. These paintings depict deities and sages in static posture or narrative mode, as well as icons associated with temples. The present contribution forms a preliminary study of this manuscript in our project on South Indian manuscripts with paintings of deities preserved in the BnF

Bayesian Programming: life science modeling and robotics applications

International audienceHow to use an incomplete and uncertain model of the environment to perceive, infer, decide and act efficiently? This is the challenge both living and artificial cognitive systems have to face. Logic is by nature unable to deal with this question. The subjectivist approach to probability is an alternative to logic specifically designed to face this challenge. In this paper we introduce Bayesian Programming, a methodology, a for- malism and an inference engine to build and compute probabilistic models. The principles are illustrated with two examples: modeling human perception of structure from motion and playing to train a video game avatar

A Bayesian tracker for synthesizing mobile robot behaviour from demonstration

International audienceProgramming robots often involves expert knowledge in both the robot itself and the task to execute. An alternative to direct programming is for a human to show examples of the task execution and have the robot perform the task based on these examples, in a scheme known as learning or programming from demonstration. We propose and study a generic and simple learning-from-demonstration framework. Our approach is to combine the demonstrated commands according to the similarity between the demonstrated sensory trajectories and the current replay trajectory. This tracking is solely performed based on sensor values and time and completely dispenses with the usually expensive step of precomputing an internal model of the task. We analyse the behaviour of the proposed model in several simulated conditions and test it on two different robotic platforms. We show that it can reproduce different capabilities with a limited number of meta parameters

A Review of Point Cloud Registration Algorithms for Mobile Robotics

International audienceThe topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration