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

Proto-communication,Social Bonding and Persuasiveness for Minimally Designed Robots Scaffolded by Non-Expert Trainers

豊橋技術科学大

Bioinspired decision-making for a socially interactive robot

Nowadays, robots and humans coexist in real settings where robots need to interact autonomously making their own decisions. Many applications require that robots adapt their behavior to different users and remember each user’s preferences to engage them in the interaction. To this end, we propose a decision making system for social robots that drives their actions taking into account the user and the robot’s state. This system is based on bio-inspired concepts, such as motivations, drives and wellbeing, that facilitate the rise of natural behaviors to ease the acceptance of the robot by the users. The system has been designed to promote the human-robot interaction by using drives and motivations related with social aspects, such as the users’ satisfaction or the need of social interaction. Furthermore, the changes of state produced by the users’ exogenous actions have been modeled as transitional states that are considered when the next robot’s action has to be selected. Our system has been evaluated considering two different user profiles. In the proposed system, user’s preferences are considered and alter the homeostatic process that controls the decision making system. As a result, using reinforcement learning algorithms and considering the robot’s wellbeing as the reward function, the social robot Mini has learned from scratch two different policies of action, one for each user, that fit the users’ preferences. The robot learned behaviors that maximize its wellbeing as well as keep the users engaged in the interactions.The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economía y Competitividad (DPI2014-57684-R); and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU (S2013/MIT-2748).Publicad

Learning the selection of actions for an autonomous social robot by reinforcement learning based on motivations

Autonomy is a prime issue on robotics field and it is closely related to decision making. Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions. Following this approach, we propose a motivational system for decision making, using internal (drives) and external stimuli for learning to choose the right action. Actions are selected from a finite set of skills in order to keep robot's needs within an acceptable range. The robot uses reinforcement learning in order to calculate the suitability of every action in each state. The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environment. The used reinforcement learning method exploits a new algorithm called Object Q-Learning. The proposed reduction of the state space and the new algorithm considering the collateral effects (relationship between different objects) results in a suitable algorithm to be applied to robots living in real environments. In this paper, a first implementation of the decision making system and the learning process is implemented on a social robot showing an improvement in robot's performance. The quality of its performance will be determined by observing the evolution of the robot's wellbeing.The funds provided by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), and the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

A biologically inspired architecture for an autonomous and social robot

Lately, lots of effort has been put into the construction of robots able to live among humans. This fact has favored the development of personal or social robots, which are expected to behave in a natural way. This implies that these robots could meet certain requirements, for example, to be able to decide their own actions (autonomy), to be able to make deliberative plans (reasoning), or to be able to have an emotional behavior in order to facilitate human-robot interaction. In this paper, the authors present a bioinspired control architecture for an autonomous and social robot, which tries to accomplish some of these features. In order to develop this new architecture, authors have used as a base a prior hybrid control architecture (AD) that is also biologically inspired. Nevertheless, in the later, the task to be accomplished at each moment is determined by a fix sequence processed by the Main Sequencer. Therefore, the main sequencer of the architecture coordinates the previously programmed sequence of skills that must be executed. In the new architecture, the main sequencer is substituted by a decision making system based on drives, motivations, emotions, and self-learning, which decides the proper action at every moment according to robot's state. Consequently, the robot improves its autonomy since the added decision making system will determine the goal and consequently the skills to be executed. A basic version of this new architecture has been implemented on a real robotic platform. Some experiments are shown at the end of the paper.This work has been supported by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid

Artificial Intelligence: Robots, Avatars, and the Demise of the Human Mediator

Published in cooperation with the American Bar Association Section of Dispute Resolutio