Représentations Ego-centrées pour la Navigation Autonome d’un Robot Humanoïde

The skill of visually approaching and positioning in relation to objects on the scene is of crucial importance for service robotics applications. Furthermore, the autonomy of the solution is essential, since human-centered scenarios, where these robots are expected to operate (e.g. at the office or home), are stochastic. Hence, it is important that the agent can react to unforeseen situations. The traditional approach of AI has not produced reliable results since it is based on extensive context-free models of the tasks, so action selection is a centralized and delayed process. Emergent models have in contrast produced fast response systems at the cost of poor generalization power, even to very similar scenarios. This research has taken an intermediate perspective between the cognitivist and the EC research. It employs simultaneously action-independent knowledge for visually recognizing the stimuli of interest, and local representations in the form of bodily sensations, in order to anticipate the consequences of action, to discriminate the object, to react to unexpected circumstances, and to assess the progress and success of the mission.La recherche sur l’automatisation du comportement a mis en évidence divers défis technologiques pour parvenir aux performances d’un système biologique. Il devient de plus en plus clair que les caractéristiques des organes sensoriels et moteurs humains sont essentiels pour atteindre certains objectifs. Malgré l’intérêt croissant en matière de solutions robotiques pour des applications de service et d’assistance, une machine qui soit polyvalente et qui imite de façon réaliste le corps anthropomorphe de l’être humain n’est pas encore disponible. Actuellement, le domaine de l’intelligence artificielle (IA) passe par des reformulations importantes. L’approche cognitiviste de l’IA n’a pas abouti à des modèles et des stratégies de représentation adaptés pour fournir un système de résolution de problème universel. Pendant les dernières décennies, la recherche en cognition incarnée (Embodied Cognition (EC)), où la représentation de la connaissance est fondée sur l’interaction physique avec l’environnement s’est développée offrant une alternative pour l’étude du comportement naturel. Toutefois, l’adoption de la méthodologie EC pose également des défis importants pour les roboticiens. Notamment, lorsqu’elle vise à satisfaire les exigences imposées par l’hypothèse du fondement physique (physical grounding hypothesis). Ainsi, son utilisation dans les applications de robotique de service n’est pas encore très développée.Cette étude a pris un point de vue intermédiaire entre la méthodologie cognitiviste et l’EC. Ce travail porte sur l’aspect architectural du comportement et se concentre sur l’exploration des sources locales d’information pour obtenir des solutions flexibles et robustes vis-à-vis des applications en robotique de service. Lors de ce travail une compétence fondamentale a été considérée comme cas d’étude : il s’agit de l’utilisation de l’ego-localisation pour se rapprocher et se positionner par rapport à des cibles visuelles. Pour cela, d’une part, on adopte l’hypothèse cognitiviste selon laquelle le robot peut se servir des représentations indépendantes-de-l’action (sous la forme de schémas perceptifs) pour faire la reconnaissance visuelle de la cible. Alors que, d’autre part, une fois que le robot s’engage dans une tâche sensorimotrice il aura recours à des représentations locales sous la forme de sensations corporelles afin d’anticiper les conséquences de l’action, de discriminer les objets, de réagir à des circonstances imprévues, d’apprendre à partir d’expériences passées, et d’évaluer le progrès et le succès de la mission. Ainsi, à partir d’une approche multidisciplinaire, ce travail porte sur différents aspects : les architectures de comportements, l’attention visuelle ascendante et descendante, la vision par ordinateur, la localisation égocentrique embarquée, la sélection d’action, l’intégration multisensorielle, et l’apprentissage par renforcement

Brazil Slide Series: Collection A Heranca Cultural De Minas Gerais, Slide No. 0049.

Teaching ResourceTeaching Resource49) View of the Escola de Minas (College of Mine Engineering) on the left, the Pico do Itacolomí in the background and the Inconfidência museum to the left. The college still has one of the best mine engineering programs in the country and students have several other degree options.49) Vista da Escola de Minas (Faculdade de Engenharia de Minas) à esquerda, o Pico do Itacolomi ao fundo, e o Museu da Inconfidência à esquerda. A Faculdade ainda possui um dos melhores cursos de engenharia de minas do país e seus estudantes têm opções para muitos outros títulos

Grounding Humanoid Visually Guided Walking: From Action-independent to Action-oriented Knowledge

International audienceIn the context of humanoid and service robotics, it is essential that the agent can be positioned with respect to objects of interest in the environment. By relying mostly on the cognitivist conception in artificial intelligence, the research on visually guided walking has tended to overlook the characteristics of the context in which behavior occurs. Consequently, considerable efforts have been directed to define action-independent explicit models of the solution, often resulting in high computational requirements. In this study, inspired by the embodied cognition research, our interest has focused on the analysis of the sensory-motor coupling. Notably, on the relation between embodiment, information, and action-oriented representation. Hence, by mimicking human walking, a behavior scheme is proposed and endowed the agent with the skill of approaching stimuli. A significant contribution to object discrimination was obtained, by exploiting the redundancies and the statistical regularities induced in the sensory-motor coordination, thus salience is anticipated from the fusion of visual and proprioceptive information in a Bayesian network. The solution was implemented on the humanoid platform Nao, where the task was accomplished in an unstructured scenario

AEGO: Modeling Attention for HRI in Ego-Sphere Neural Networks

Accepted for publication in the 2024 IEEE/RSJ International Conference on Intelligent Robots and SystemsInternational audienceDespite important progress in recent years, social robots are still far away from showing advanced behavior for interaction and adaptation in human environments. Thus, we are interested in studying social cognition in human-robot interaction (HRI), notably in improving communication skills relying on joint attention (JA) and knowledge sharing. Since JA involves low-level cognitive processes in humans, we take into account the implications of Moravec's Paradox and focus on the aspect of knowledge representation. Inspired by 4E cognition principles, we study egocentric localization through the concept of sensory ego-sphere. We propose a neural network architecture named AEGO to model attention for each agent in interaction and show how to fuse information in a common representation space. From the perspective of dynamic fields theory, AEGO takes into account the dynamics of bottom-up and top-down modulation processes and the effects of neural excitatory and inhibitory synaptic interaction. In this work we evaluate the model in simulation and experiments with the robot Pepper in JA tasks based on proprioception, vision, rudimentary natural language and Hebbian plasticity. Results show that AEGO is convenient for HRI, allowing the human and the robot to share attention and knowledge about objects in scenarios close to everyday situations. AEGO constitutes a novel brain-inspired architecture to model attention that is suitable for multi-agent applications relying on social cognition skills, having the potential to generalize to several robotics platforms and HRI scenarios

A Computational Cognition and Visual Servoing Based Methodology to Design Automatic Manipulative Tasks

International audienceIn the last decades, robotics has exerted an important role in the research on diverse knowledge domains, such as, artificial intelligence, biology, neuroscience and psychology. In particular, the study of knowledge representation and thinking, has led to the proposal of cognitive architectures; capturing essential structures and processes of cognition and behavior. Robotists have also attempted to design automatic systems using these proposals. Though, certain difficulties have been reported for obtaining efficient low-level processing while sensing or controlling the robot. The main challenges involve the treatment of the differences between the computational paradigms employed by the cognitive and the robotic architectures. The objective of this work, is to propose a methodology for designing robotic systems capable of decision making and learning when executing manipulative tasks. The development of a system called the Cognitive Reaching Robot (CRR) will be reported. CRR combines the advantages of using a psychologically-oriented cognitive architecture, with efficient low-level behavior implementations through the visual servoing control technique

TOP-JAM: A bio-inspired topology-based model of joint attention for human-robot interaction

International audienceCoexisting with others and interacting in society implies sharing knowledge and attention about world objects, events, features, episodes, and even imagination or abstract ideas in time and space. Inspired by human phenomenological, cognitive and behavioral research, this work focuses on the study of joint attention (JA) for human-robot interaction (HRI), based on two main assumptions: a) the perception and representation of attention jointness constitute an isomorphic relation, and b) inspiration on dynamic neural fields (DNF) theory is a promising way to investigate contextual and non-linear spatio-temporal relations underlying attention and knowledge sharing in HRI. Taking into account the previous considerations, we propose a topology-based model for JA named TOP-JAM, which is able to represent and track in real-time JA states, from observations of behavioral data. More importantly, the model consists in a representation that can be directly understood by human beings, which conforms to robo-ethical principles in social robotics. This study evaluates computational properties of the model in simulation. Through a real experiment with the robot Pepper, the study shows that TOP-JAM is able to track JA in a triad interaction scenario

Sorry I overreacted: The role of Affect in the Modulation of Motor Resonance during Face-to-Face Interaction

International audienceGrowing research converge face-to-face interaction mutually influences spontaneous postures and gestures to show. This phenomenon is referred to as motor resonance. Studies on psychotherapy sessions, reported spontaneous and mirrored body movements emerged in therapists and patients (Ramseyer & Tschacher, 2011). However, affective context may play a role in the strength motor resonance. Based on the philosophical framework by Mühlhoff (2015), we propose to test the affective resonance theory. In a face-to-face study, we used the social robot Nao to investigate the role of affective context (i.e., positive or negative) in triggering motor resonance in humans. Nao was programed to exhibit cowering (sad) or open (happy) body language; Nao moved slow or fast to exhibit energy levels. Participants attended a single experimental session during which Nao moved slow and fast, while it narrated a sad or happy stories. Concurrently, the kinematic activity of each participant was recorded using a motion-capture system. Additional measures included self-reported affect, and a questionnaire pertaining to the robot’s social attributes.Results show that more human body movements and sway were measured when Nao moved fast than when it moved slow. Additionally, movement patterns were influenced by the affective context. Overall, our results indicate that core affect can influence interaction dynamics. Future studies manipulating emotional context need to be conducted now in human-human interaction to generalize the affective resonance theory to social interactio

A dynamic computational model of motivation based on self-determination theory and CANN

International audienc