Evolutionary Speech Recognition

Automatic speech recognition systems are becoming ever more common and are increasingly deployed in more variable acoustic conditions, by very different speakers. So these systems, generally conceived in a laboratory, must be robust in order to provide optimal performance in real situations. This article explores the possibility of gaining robustness by designing speech recognition systems able to auto-modify in real time, in order to adapt to the changes of acoustic environment. As a starting point, the adaptive capacities of living organisms were considered in relation to their environment. Analogues of these mechanisms were then applied to automatic speech recognition systems. It appeared to be interesting to imagine a system adapting to the changing acoustic conditions in order to remain effective regardless of its conditions of use

Game theoretic decision making for autonomous vehicles’ merge manoeuvre in high traffic scenarios

International audienc

Contribution à la navigation autonome en environnement dynamique et humain

La navigation autonome en environnement dynamique et humain représente encore un défi important pour la recherche en robotique. Le point central du problème est de garantir la sécurité de tous les agents qui se déplacent dans l'espace. Contrairement aux environnements statiques ou contrôlés, où les techniques de planification globale peuvent être adoptées, les environnements dynamiques présentent des difficultés majeures : la détection et le suivi des obstacles mobiles, la prédiction de l'état futur du monde, la planification et la navigation en ligne. Si l'on rajoute les contraintes liées à la présence d'humains dans la scène, on se confronte alors à la problématique des conventions sociales, de la compréhension, de la modélisation et de la prédiction des intentions et des comportements humains. Ce mémoire d'Habilitation à Diriger des Recherches présente mes principales activités de recherche menées depuis 2002, année de recrutement comme maître de conférences à l'Université Pierre-Mendès-France. Ce document fait la synthèse de mes contributions dans le domaine de la navigation des robots dans des environnements dynamiques et humains et s'organise autour de 3 thématiques, à savoir : 1. La modélisation d'environnements dynamiques et humains dans des grilles probabilistes. La contribution porte à la fois sur la détection et le tracking d'objets dynamiques et sur la compréhension de scènes sociales. 2. La navigation de robots mobiles en environnement dynamique et incertain. La contribution porte sur l'intégration d'une représentation probabiliste de l'environnement dans la navigation en abordant la notion de risque de collisions et de respect de conventions sociales. 3. La prise en compte des habitudes et des intentions de l'utilisateur passager d'un robot mobile (ici un fauteuil roulant) pour la navigation

Social Mapping of Human-Populated Environments by Implicit Function Learning

International audienceWith robots technology shifting towards entering human populated environments, the need for augmented perceptual and planning robotic skills emerges that complement to human presence. In this integration, perception and adaptation to the implicit human social conventions plays a fundamental role. Toward this goal, we propose a novel framework that can model context-dependent human spatial interactions, encoded in the form of a social map. The core idea of our approach resides in modelling human personal spaces as non-linearly scaled probability functions within the robotic state space and devise the structure and shape of a social map by solving a learning problem in kernel space. The social borders are subsequently obtained as isocontours of the learned implicit function that can realistically model arbitrarily complex social interactions of varying shape and size. We present our experiments using a rich dataset of human interactions, demonstrating the feasibility and utility of the proposed approach and promoting its application to social mapping of human-populated environments

On Leader Following and Classification

International audienceService and assistance robots that must move in human environment must address the difficult issue of navigating in dynamic environments. As it has been shown in previous works, in such situations the robots can take advantage of the motion of persons by following them, managing to move together with humans in difficult situations. In those circumstances, the problem to be solved is how to choose a human leader to be followed. This work proposes an innovative method for leader selection, based on human experience. A learning framework is developed, where data is acquired, labeled and then used to train an AdaBoost classification algorithm, to determine if a candidate is a bad or a good leader, and also to study the contribution of features to the classification process

Using social cues to estimate possible destinations when driving a robotic wheelchair

International audienceApproaching a group of humans is an important navigation task. Although many methods have been proposed to avoid interrupting groups of people engaged in a conversation, just a few works have considered the proper way of joining those groups. Research in the field of social sciences have proposed geometric models to compute the best points to join a group. In this article we propose a method to use those points as possible destinations when driving a robotic wheelchair. Those points are considered together with other possible destinations in the environment such as points of interest or typical static destinations defined by the user's habits. The intended destination is inferred using a Dynamic Bayesian Network that takes into account the contextual information of the environment and user's orders to compute the probability for each destination

Multimodal Control of a Robotic Wheelchair: Using Contextual Information for Usability Improvement

International audienceIn this paper, a method to perform semi-autonomous navigation on a wheelchair is presented. The wheelchair could be controlled in semi-autonomous mode estimating the user's intention by using a face pose recognition system or in manual mode. The estimator was performed within a Bayesian network approach. To switch these two modes, a speech interface was used. The user's intention was modeled as a set of typical destinations visited by the user. The algorithm was implemented to one experimental wheelchair robot. The new application of the wheelchair system with more natural and easy-to-use human machine interfaces was one of the main contributions. as user's habits and points of interest are employed to infer the user's desired destination in a map. Erroneous steering signals coming from the user- machine interface input are filtered out, improving the overall performance of the system. Human aware navigation, path planning and obstacle avoidance are performed by the robotic wheelchair while the user is just concerned with "looking where he wants to go"

Experiments in Leader Classification and Following with an Autonomous Wheelchair

International audienceWith decreasing costs in robotic platforms, mobile robots that provide assistance to humans are becoming a reality. A key requirement for these types of robots is the ability to efficiently and safely navigate in populated environments. This work proposes to address this issue by studying how robots can select and follow human leaders, to take advantage of their motion in complex situations. To accomplish this, a machine learning framework is proposed, comprising data acquisition with a real robot, data labeling, feature extraction and the training of a leader classifier. Preliminary experiments combined the classification system with a multi-mode navigation algorithm, to validate this approach using an autonomous wheelchair

An hybrid simulation tool for autonomous cars in very high traffic scenarios

International audienceThis article introduces an open source tool for simulating autonomous vehicles in complex, high traffic, scenarios. The proposed approach consists on creating an hybrid simulation, which fully integrates and synchronizes two well known simulators: a microscopic, multi-modal traffic simulator and a complex 3D simulator. The presented software tool allows to simulate an autonomous vehicle, including all its dynamics, sensors and control layers, in a scenario with a very high volume of traffic. The hybrid simulation creates a bi-directional integration, meaning that, in the 3D simulator, the ego-vehicle sees and interacts with the rest of the vehicles, and at the same time, in the traffic simulator, all additional vehicles detect and react to the actions of the ego-vehicle. Two interfaces, one for each simulator, where created to achieve the integration, they ensure the synchronization of the scenario, the state of all vehicles including the ego-vehicle, and the time. The capabilities of the hybrid simulation was tested with different models for the ego-vehicle and almost 300 additional vehicles in a complex merge scenario