Towards a framework for socially interactive robots

250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa línea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guía y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creíbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo físico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para así poder mejorar su sociabilida

The DREAM Dataset: Supporting a data-driven study of autism spectrum disorder and robot enhanced therapy

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children’s behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.CC BY 4.0DREAM - Development of robot-enhanced therapy for children with autism spectrum disorders

Look me in the eyes: A survey of eye and gaze animation for virtual agents and artificial systems

International audienceA person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: "The face is the portrait of the mind; the eyes, its informers.". This presents a huge challenge for computer graphics researchers in the generation of artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human-human interactions. This State of the Art Report provides an overview of the efforts made on tackling this challenging task. As with many topics in Computer Graphics, a cross-disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We discuss the movement of the eyeballs, eyelids, and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Further, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye-gaze, during the expression of emotion or during conversation, and how they are synthesised in Computer Graphics and Robotics

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

The Impact of Robot Tutor Social Behaviour on Children

Robotic technologies possess great potential to enter our daily lives because they have the ability to interact with our world. But our world is inherently social. Whilst humans often have a natural understanding of this complex environment, it is much more challenging for robots. The field of social Human-Robot Interaction (HRI) seeks to endow robots with the characteristics and behaviours that would allow for intuitive multimodal interaction. Education is a social process and previous research has found strong links between the social behaviour of teachers and student learning. This therefore presents a promising application opportunity for social human-robot interaction. The thesis presented here is that a robot with tailored social behaviour will positively influence the outcomes of tutoring interactions with children and consequently lead to an increase in child learning when compared to a robot without this social behaviour. It has long been established that one-to-one tutoring provides a more effective means of learning than the current typical school classroom model (one teacher to many students). Schools increasingly supplement their teaching with technology such as tablets and laptops to offer this personalised experience, but a growing body of evidence suggests that robots lead to greater learning than other media. It is posited that this is due to the increased social presence of a robot. This work adds to the evidence that robots hold a social advantage over other technological media, and that this indeed leads to increased learning. In addition, the work here contributes to existing knowledge by seeking to expand our understanding of how to manipulate robot social behaviour in educational interactions such that the behaviour is tailored for this purpose. To achieve this, a means of characterising social behaviour is required, as is a means of measuring the success of the behaviour for the interaction. To characterise the social behaviour of the robot, the concept of immediacy is taken from the human-human literature and validated for use in HRI. Greater use of immediacy behaviours is also tied to increased cognitive learning gains in humans. This can be used to predict the same effect for the use of social behaviour by a robot, with learning providing an objective measure of success for the robot behaviour given the education application. It is found here that when implemented on a robot in tutoring scenarios, greater use of immediacy behaviours generally does tend to lead to increased learning, but a complex picture emerges. Merely the addition of more social behaviour is insufficient to increase learning; it is found that a balance should be struck between the addition of social cues, and the congruency of these cues