Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders whose core symptoms are impairments in socio-communication and repetitive interests and stereotypies. Although motor impairments are not cardinal symptoms per se, many children with ASD have postural and motor coordination disabilities. Here, we investigate them dynamically through human-machine interaction, analysing data previously collected during two experiments: a joint attention (JA) task involving a human-robot interaction with children with ASD and typically developing (TD) controls; a motor imitation task involving a tightrope walker (TW) avatar, children with ASD, with developmental coordination disorder (DCD) and TD children. Participants were recorded using RGB and RGB-D cameras, allowing for the extraction of kinematic and spectral features. Movement power spectral density (MPSD) analysis demonstrated that children with ASD displayed more energetically costly behaviors across all frequencies when contrasted with TD children (both experiments) and children with DCD (TW experiment). These findings affirm the distinct motor complexity of children with ASD, suggesting the presence of a unique motor signature potentially different from that observed in children with DCD.

In humans, changes in the visual field or obstructions influence the perception and interpretation of facial emotions. A partial or altered visual area can lead to a misunderstanding of the facial expressions of the interlocutor. This problem also arises in the field of social robotics, where the visual perception of robots is often constrained by technical and environmental factors. With the rise of robots intended for the general public, this is a question that must now be taken into account in many common situations. In this study, we explore the impact of restricting the visual field of a robot equipped with developmental AI on its ability to recognize human facial emotions. We analyze how different limitations of the field of vision affect its reactions and modify its ability to recognize facial emotions. For this, we developed A architecture to explore this question, based on: (1) the recognition of primary emotions through an imitation game that enhances the robot’s ability to recognize emotions, and (2) an attention-focusing mechanism. The integration of these two components allows the robot to modulate its areas of interest before performing facial emotion recognition. This approach enables the exploration of various recognition scenarios, ranging from full-face analysis to a more targeted focus on the mouth—an area particularly emphasized by elderly individuals and bilingual children. Our results show that the restriction of the visual field leads to a decrease in the accuracy of emotion recognition, with variations depending on the type of expression and the degree of limitation of the visual field. These results are consistent with observations made in humans, suggesting the possibility of reproducing this mechanism in social robotics.

Social robotics continues to expand as a prominent area of research due to the increasing use of robots in settings involving social interactions. In this paper we study the social dimension of human robot interaction during a posture imitation game. We discuss how human personality traits influence a learning robot. A neural architecture is used to enable autonomous and interactive learning. We address this issue by linking the performance of the robot algorithms (learning trajectory, convergence, recognition score...) to the personality traits of its partners (extraversion and anxiety). As results of this work we noticed the emergence of pattern related to the extraversion trait of individuals interacting with the robot but no evidence for anxiety. By analyzing the robot's learning with each partner, we observe that the acquisition time of the partner's visual representation was correlated with the extraversion trait. The results show that even in a simple posture imitation game, we observe a fluctuation in a robot's learning based on the personality traits exhibited by its human partner.

A social individual needs to effectively manage the amount of complex information in his or her environment relative to his or her own purpose to obtain relevant information. This paper presents a neural architecture aiming to reproduce attention mechanisms (alerting/orienting/selecting) that are efficient in humans during audiovisual tasks in robots. We evaluated the system based on its ability to identify relevant sources of information on faces of subjects emitting vowels. We propose a developmental model of audio-visual attention (MAVA) combining Hebbian learning and a competition between saliency maps based on visual movement and audio energy. MAVA effectively combines bottom-up and top-down information to orient the system toward pertinent areas. The system has several advantages, including online and autonomous learning abilities, low computation time and robustness to environmental noise. MAVA outperforms other artificial models for detecting speech sources under various noise conditions.

The SAB 2022 proceedings focus on models of adaptive behavior in real animals and in synthetic agents, such as robot models, computer simulation models.

In order to keep trace of information and grow up, the infant brain has to resolve the problem about where old information is located and how to index new ones. We propose that the immature prefrontal cortex (PFC) uses its primary functionality of detecting hierarchical patterns in temporal signals as a second feature to organize the spatial ordering of the cortical networks in the developing brain itself. Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the shape of ordinal patterns and uses them to index information hierarchically in different parts of the brain. Henceforth, we propose that this mechanism for detecting ordinal patterns participates also in the hierarchical organization of the brain during development; i.e., the bootstrapping of the connectome. By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and for planning temporally ordered information; i.e., the emergence of causality and symbolic thinking. In this position paper, we will review several neural models from the literature that support serial ordering and propose an original one. We will confront then our ideas with evidence from developmental, behavioral, and brain results.

We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones).

We propose a global framework for modeling the cortico-basal system (CX-BG) and the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences; ie, sound perception and speech production. Our genuine model is based on the neural architecture called INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy (FE) corresponds to the prediction error on internal or external noise. FE minimization is used for exploring, selecting and learning in PFC the optimal choices of actions to perform in the BG network (eg sound production) in order to reproduce and control the most accurately possible the spike trains representing sounds in CX. The difference between the two working memories relies in the neural coding itself, which is based on temporal ordering in the CX-BG networks (Spike Timing-Dependent Plasticity) and on the rank ordering in the sequence in the PFC-BG networks (gating or gain-modulation). We detail in this short paper the CX-BG system responsible to encode the audio primitives at few milliseconds order, and the PFC-BG system responsible for the learning of temporal structure in sequences. Two experiments done with a small and a big audio database show the capabilities of exploration, generalization and robustness to noise of the neural architecture to retrieve audio primitives as well as long-range sequences based on structure detection. Although both learning mechanisms are implemented with the same algorithm of rank-order coding, the CX-BG system realizes a model-free recurrent neural network (INFERNO) and the PFC-BG system implements a gated recurrent neural network (INFERNO GATE).

In order to keep trace of information and grow up, the infant brain has to resolve the problem about where old information is located and how to index new ones. We propose that the immature prefrontal cortex (PFC) use its primary functionality of detecting hierarchical patterns in temporal signals as a second purpose to organize the spatial ordering of the cortical networks in the developing brain itself. Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the form of ordinal patterns and use them to index information hierarchically in different parts of the brain. Henceforth, we propose that this mechanism for detecting patterns participates in the ordinal organization development of the brain itself; i.e., the bootstrapping of the connectome. By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and planning temporally ordered information; i.e., the emergence of symbolic thinking and language. We will review neural models that can support such mechanisms and propose new ones. We will confront then our ideas with evidence from developmental, behavioral and brain results and make some hypotheses, for instance, on the construction of the mirror neuron system, on embodied cognition, and on the capacity of learning-to-learn.

We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in the PFC, we propose a genuine coding strategy using the gain-modulation mechanism to represent abstract sequences based solely on the rank and location of items within them. Based on this mechanism, we show that we can construct a repertoire of neurons sensitive to the temporal structure in sequences from which we can represent any novel sequences. Free-energy optimization is then used to explore and to retrieve the missing indices of the items in the correct order for executive control and compositionality. We show that the gain-modulation mechanism permits the network to be robust to variabilities and to have long-term dependencies as it implements a gated recurrent neural network. This model, called Inferno Gate, is an extension of the neural architecture Inferno standing for Iterative Free-Energy Optimization of Recurrent Neural Networks with Gating or Gain-modulation. In experiments performed with an audio database of ten thousand MFCC vectors, Inferno Gate is capable of encoding efficiently and retrieving chunks of fifty items length. We then discuss the potential of our network to model the features of working memory in the PFC-BG loop for structural learning, goal-direction and hierarchical reinforcement learning.

This paper presents a sensory-motor architecture based on a neural network allowing a robot to recognize vowels in a multi-modal way thanks to human mimicking. The robot autonomously learns to associate its internal state to a humanu0027s vowel as an infant would to recognize vowel, and learn to associate congruent information.

Adults readily make associations between stimuli perceived consecutively through different sense modalities, such as shapes and sounds. Researchers have only recently begun to investigate such correspondences in infants but only a handful of studies have focused on infants less than a year old. Are infants able to make cross-sensory correspondences from birth? Do certain correspondences require extensive real-world experience? Some studies have shown that newborns are able to match stimuli perceived in different sense modalities. Yet, the origins and mechanisms underlying these abilities are unclear. The present paper explores these questions and reviews some hypotheses on the emergence and early development of cross-sensory associations and their possible links with language development. Indeed, if infants can perceive cross-sensory correspondences between events that share certain features but are not strictly contingent or co-located, one may posit that they are using a ‘sixth sense’ in Aristotle’s sense of the term. And a likely candidate for explaining this mechanism, as Aristotle suggested, is movement.

L’objectif de cet article est de presenter une approche pedagogie basee sur l’intrapreneuriat qui est une approche transversale par l'acquisition de competences metiers. Elle vise a emuler la creativite, des savoirs et savoir-faire au sein d’espaces innovants (FacLab, living Lab, …) et dans un contexte intrapreneurial. Ces competences necessitent que chacun des acteurs integrent les contours et mecanismes des autres partenaires. Cette approche a ete implementee a l’IUT de Cergy-Pontoise (departement GEII Sarcelles) et s’articule sur une periode de 3 annees incluant le DUT et la licence professionnelle IMNEOV. La finalite de ce parcours vise a former des techniciens superieurs aux metiers du numerique. La methode adoptee est basee sur une pedagogie active par projet ou l’enseignant s’adapte au niveau de l’apprenant.

We propose a unified framework for modeling the cortico-basal system (CX-BG) and the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences; ie, sound perception and speech production. Our genuine model is based on the neural architecture called INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy (noise) minimization is used for exploring, selecting and learning in PFC the optimal choices of actions to perform in the BG network (eg sound production) in order to reproduce and control the most accurately possible the spike trains representing sounds in CX. The difference between the two working memories relies in the neural coding itself, which is based on temporal ordering in the CX-BG networks (Spike Timing-Dependent Plasticity) and on the rank ordering in the sequence in the PFC-BG networks (gating or gain-modulation). We detail in this paper only the CX-BG system responsible to encode the audio primitives at few milliseconds order, while the PFC-BG system responsible for the learning of temporal structure in sequences will be presented in a complementary paper. Two experiments done with a small and a big audio database show the capabilities of exploration, generalization and robustness to noise of the neural architecture to retrieve audio primitives.

Background. - Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders which core symptoms are impairments in socio-communication and repetitive symptoms and stereotypies. Although not cardinal symptoms per se, motor impairments are fundamental aspects of ASD. These impairments are associated with postural and motor control disabilities that we investigated using computational modeling and developmental robotics through human-machine interaction paradigms. Method. - First, in a set of studies involving a human-robot posture imitation, we explored the impact of 3 different groups of partners (including a group of children with ASD) on robot learning by imitation. Second, using an ecological task, i.e. a real-time motor imitation with a tightrope walker (TW) avatar, we investigated interpersonal synchronization, motor coordination and motor control during the task in children with ASD (n = 29), TD children (n = 39) and children with developmental coordination disorder (n = 17, DCD). Results. - From the human-robot experiments, we evidenced that motor signature at both groups' and individuals' levels had a key influence on imitation learning, posture recognition and identity recognition. From the more dynamic motor imitation paradigm with a TW avatar, we found that interpersonal synchronization, motor coordination and motor control were more impaired in children with ASD compared to both TD children and children with DCD. Taken together these results confirm the motor peculiarities of children with ASD despite imitation tasks were adequately performed. Discussion. - Studies from human-machine interaction support the idea of a behavioral signature in children with ASD. However, several issues need to be addressed. Is this behavioral signature motoric in essence? Is it possible to ascertain that these peculiarities occur during all motor tasks (e.g. posture, voluntary movement)? Could this motor signature be considered as specific to autism, notably in comparison to DCD that also display poor motor coordination skills? We suggest that more work comparing the two conditions should be implemented, including analysis of kinematics and movement smoothness with sufficient measurement quality to allow spectral analysis. (C) 2018 L'Encephale, Paris.

In this paper we explore the dynamics of Joint Attention (JA) in children with Autism Spectrum Disorder (ASD) during an interaction task with a small humanoid robot. While this robot elicits JA in children, a coupled perception system based on RGB-D sensors is able to capture their behaviours. The proposed system shows the feasibility and the practical benefits of the use of social robots as assessment tools of ASD. We propose a set of measures to describe the behaviour of the children in terms of body and head movements, gazing magnitude, gazing directions (left vs. front vs. right) and kinetic energies. We assessed these metrics by comparing 42 children with ASD and 16 children with typical development (TD) during the JA task with the robot, highlighting significant differences between the two groups. Employing the same metrics, we also assess a subgroup of 14 children with ASD after 6-month of JA training with a serious game. The longitudinal data confirms the relevance of the proposed metrics as they reveal the improvements of children behaviours after several months of training. (C) 2018 Elsevier B.V. All rights reserved.

In this work, we study how learning in a special environment such as a museum can influence the behavior of robots. More specifically, we show that online learning based on interaction with people at a museum leads the robots to develop individual preferences. We first developed a humanoid robot (Berenson) that has the ability to head toward its preferred object and to make a facial expression that corresponds to its attitude toward said object. The robot is programmed with a biologically-inspired neural network sensory-motor architecture. This architecture allows Berenson to learn and to evaluate objects. During experiments, museum visitors’ emotional responses to artworks were recorded and used to build a database for training. A similar database was created in the laboratory with laboratory objects. We use those databases to train two simulated populations of robots. Each simulated robot emulates the Berenson sensory-motor architecture. Firstly, the results show the good performance of our architecture in artwork recognition in the museum. Secondly, they demonstrate the effect of training variability on preference diversity. The response of the two populations in a new unknown environment is different; the museum population of robots shows a greater variance in preferences than the population of robots that have been trained only on laboratory objects. The obtained diversity increases the chances of success in an unknown environment and could favor an accidental discovery.

In this paper we propose a neural network allowing a mobile robot to learn artwork appreciation. The learning is based on the social referencing approach. The robot acquires its knowledge (artificial taste) from the interaction with humans. We present and analyze specifically the visual system, its impact on the robot behavior, and at the end, we analyze the readability of our robot behavior according to visitors comments. We show that the low level spatial competition between the values associated to areas of interest in the image are important for the coherence of the robot's object evaluation and the readability of its behavior.

Italy boucenna@isir.umpc.fr Abstract —Recently, there have been considerable advances in the research on innovative information communication technology (ICT) for the education of people with autism. This review focuses on two aims: (1) to provide an overview of the recent ICT applications used in the treatment of autism and (2) to focus on the early development of imitation and joint attention in the context of children with autism as well as robotics. There have been a variety of recent ICT applications in autism, which include the use of interactive environments implemented in computers and special input devices, virtual environments, avatars and serious games as well as telerehabilitation. Despite exciting preliminary results, the use of ICT remains limited. Many of the existing ICTs have limited capabilities and performance in actual interactive conditions. Clinically, most ICT proposals have not been validated beyond proof of concept studies. Robotics systems, developed as interactive devices for children with autism, have been used to assess the child’s response to robot behaviors; to elicit behaviors that are promoted in the child; to model, teach and practice a skill; and to provide feed-back on performance in specific environments (e.g., therapeutic sessions). Based on their importance for both early development and for building autonomous robots that have human-like abilities, imitation, joint attention and interactive engagement are key