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International audience; The inclusion of adolescents with Autism Spectrum Disorders (ASD) in mainstream environments, especially mainstream classrooms, is critically impeded by their difficulties in self-regulating their emotions. Albeit new technologies have shown tremendous emulation in the field of ASD, solutions to assist individuals with ASD in emotion regulation appear very limited. This paper addresses this critical challenge through the design and evaluation of a tablet-based application dedicated to supporting adolescents with ASD in self-regulating their emotions in mainstream inclusive classrooms. This system relies on well-proven (paper-based) emotion-regulation interventions reported by special-education teachers and families through a participatory design. The experiment included 14 adolescents with ASD (as well as 19 adolescents with Intellectual Disabilities) using the application during three months in mainstream schools. We compared these two groups with another group of 15 adolescents with ASD who were not equipped with our application. Usability performance as well as efficacy performance (emotion-regulation outcomes) have been investigated in situ. We showed that 1) our system is autonomously and successfully used in mainstream classrooms, 2) it is also an efficient support for adolescents with ASD to self-regulate their emotions. This paper demonstrates that emotion regulation of adolescents with ASD can be supported within mainstream classrooms through an assistive technology, in order to improve both behaviors and sociocognitive processes linked with core features of ASD. Moreover, it provides insights of a methodology suited to mainstream classrooms, building on previous research addressing specialized settings.

International audience; The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, we describe a method for learning and incrementally updating a model of visual saliency from a depth-based object detector. This model of saliency can also be exploited to produce bounding box proposals around objects of interest. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, called Reinforcement Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that RL-IAC can drastically decrease the required time for learning a reliable saliency model.

International audience; This article discusses open scientific challenges for understanding development and evolution of speech forms , as a commentary to Moulin-­Frier et al. (Moulin-­Frier et al., 2015). Based on the analysis of mathematical models of the origins of speech forms, with a focus on their assumptions , we study the fundamental question of how speech can be formed out of non-­‐speech , at both developmental and evolutionary scales. In particular , we emphasize the importance of embodied self-­organization , as well as the role of mechanisms of motivation and active curiosity-­driven exploration in speech formation. Finally , we discuss an evolutionary-­‐developmental perspective of the origins of speech.

International audience; Recent psychophysical evidence in humans suggests that visual attention is a highly dynamic and predictive process involving precise models of object tra-jectories. We present a proof-of-concept that such predictive spatial attention can benefit a technical system solving a challenging visual object detection task. To this end, we introduce a Bayes-like integration of so-called dynamic attention priors (DAPs) and dense detection likelihoods, which get enhanced at predicted object positions obtained by the extrapolation of trajectories. Using annotated video sequences of pedestrians in a parking lot setting, we quantitatively show that DAPs can improve detection performance significantly as compared to a baseline condition relying purely on pattern analysis.

In this article, we explore the potential contribution of multimodal context information to object detection in an "intelligent car". The used car platform incorporates subsystems for the detection of objects from local visual patterns, as well as for the estimation of global scene properties (sometimes denoted "scene context" or just "context") such as the shape of the road area or the 3D position of the ground plane. Annotated data recorded on this platform is publicly available as the "HRI Road Traffic" vehicle video dataset, which forms the basis for this investigation. In order to quantify the contribution of context information, we investigate whether it can be used to infer object identity with little or no reference to local patterns of visual appearance. Using a challenging vehicle detection task based on the "HRI Road Traffic" dataset, we train selected algorithms ("context models") to estimate object identity from context information alone. In the course of our performance evaluations, we also analyze the effect of typical real-world conditions (noise, high input dimensionality, environmental variation) on context model performance. As a principal result, we show that the learning of context models is feasible with all tested algorithms, and that object identity can be estimated from context information with similar accuracy as by relying on local pattern recognition methods. We also find that the use of basis function representations [1] (also known as "population codes") allows the simplest (and therefore most efficient) learning methods to perform best in the benchmark, suggesting that the use of context is feasible even in systems operating under strong performance constraints. (C) 2012 Elsevier B.V. All rights reserved.

International audience; This paper presents algorithms that allow a robot to express its emotions by modulating the intonation of its voice. They are very simple and efficiently provide life-like speech thanks to the use of concatenative speech synthesis. We describe a technique which allows to continuously control both the age of a synthetic voice and the quantity of emotions that are expressed. Also, we present the first large-scale data mining experiment about the automatic recognition of basic emotions in informal everyday short utterances. We focus on the speaker-dependent problem. We compare a large set of machine learning algorithms, ranging from neural networks, Support Vector Machines or decision trees, together with 200 features, using a large database of several thousands examples. We show that the difference of performance among learning schemes can be substantial, and that some features which were previously unexplored are of crucial importance. An optimal feature set is derived through the use of a genetic algorithm. Finally, we explain how this study can be applied to real world situations in which very few examples are available. Furthermore, we describe a game to play with a personal robot which facilitates teaching of examples of emotional utterances in a natural and rather unconstrained manner. Keywords: Emotions; Speech; Robots; Emotion production; Emotion recognition