Prosody based emotion recognition for MEXI

Abstract — This paper describes the emotion recognition from natural speech as realized for the robot head MEXI. We use a fuzzy logic approach for analysis of prosody in natural speech. Since MEXI often communicates with well known persons but also with unknown humans, for instance at exhibitions, we realized a speaker dependent mode as well as a speaker independent mode in our prosody based emotion recognition. A key point of our approach is that it automatically selects the most significant features from a set of twenty analyzed features based on a training database of speech samples. This is important according to our results, since the set of significant features differs considerably between the distinguished emotions. With our approach we reach average recognition rates of 84 % in speaker dependent mode and 60 % in speaker independent mode. Index Terms — Emotion recognition, prosody, fuzzy rules, robot hea

Bio-inspired decision making system for an autonomous social robot: the role of fear

Robotics is an emergent field which is currently in vogue. In the near future, many researchers anticipate the spread of robots coexisting with humans in the real world. This requires a considerable level of autonomy in robots. Moreover, in order to provide a proper interaction between robots and humans without technical knowledge, these robots must behave according to the social and cultural norms. This results in social robots with cognitive capabilities inspired by biological organisms such as humans or animals. The work presented in this dissertation tries to extend the autonomy of a social robot by implementing a biologically inspired decision making system which allows the robot to make its own decisions. Considering this kind of decision making system, the robot will not be considered as a slave any more, but as a partner. The decisionmaking systemis based on drives,motivations, emotions, and self-learning. According to psychological theories, drives are deficits of internal variables or needs (e.g. energy) and the urge to correct these deficits are the motivations (e.g. survival). Following a homeostatic approach, the goal of the robot is to satisfy its drives maintaining its necessities within an acceptable range, i.e. to keep the robot’s wellbeing as high as possible. The learning process provides the robot with the proper behaviors to cope with each motivation in order to achieve the goal. In this dissertation, emotions are individually treated following a functional approach. This means that, considering some of the different functions of emotions in animals or humans, each artificial emotion plays a different role. Happiness and sadness are employed during learning as the reward or punishment respectively, so they evaluate the performance of the robot. On the other hand, fear plays a motivational role, that is, it is considered as a motivation which impels the robot to avoid dangerous situations. The benefits of these emotions in a real robot are detailed and empirically tested. The robot decides its future actions based on what it has learned from previous experiences. Although the current context of this robot is limited to a laboratory, the social robot cohabits with humans in a potentially non-deterministic environment. The robot is endowed with a repertory of actions but, initially, it does not know what action to execute either when to do it. Actually, it has to learn the policy of behavior, i.e. what action to execute in different world configuration, that is, in every state, in order to satisfy the drive related to the highest motivation. Since the robot will be learning in a real environment interacting with several objects, it is desired to achieve the policy of behavior in an acceptable range of time. The learning process is performed using a variation of the well-known Q-Learning algorithm, the Object Q-Learning. By using this algorithm, the robot learns the value of every state-action pair through its interaction with the environment. This means, it learns the value that every action has in every possible state; the higher the value, the better the action is in that state. At the beginning of the learning process these values, called the Q values, can all be set to the same value, or some of them can be fixed to another value. In the first case, this implies that the robot will learn from scratch; in the second case, the robot has some kind of previous information about the action selection. These values are updated during the learning process. The emotion of fear is particularly studied. The generation process of this emotion (the appraisal) and the reactions to fear are really useful to endow the robot with an adaptive reliable mechanism of “survival”. This dissertation presents a social robot which benefits from a particular learning process of new releasers of fear, i.e. the capacity to identify new dangerous situations. In addition, by means of the decision making system, the robot learns different reactions to prevent danger according to different unpredictable events. In fact, these reactions to fear are quite similar to the fear reactions observed in nature. Another challenge is to design a solution for the decision making system in such a way that it is flexible enough to easily change the configuration or even apply it to different robots. Considering the bio-inspiration of this work, this research (and other related works) was born as a try to better understand the brain processes. It is the author’s hope that it sheds some light in the study of mental processes, in particular those which may lead to mental or cognitive disorders. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La robótica es un área emergente que actualmente se encuentra en boga. Muchos científicos pronostican que, en un futuro próximo, los robot cohabitarán con las personas en el mundo real. Para que esto llegue a suceder, se necesita que los robots tengan un nivel de autonomía considerable. Además, para que exista una interacción entre robots y personas sin conocimientos técnicos, estos robots deben comportarse de acuerdo a las normas sociales y culturales. Esto nos lleva a robots sociales con capacidades cognitivas inspiradas en organismos biológicos, como los humanos o los animales. El trabajo que se presenta en esta tesis pretende aumentar la autonomía de un robot social mediante la implementación de un sistema de toma de decisiones bioinspirado que permita a un robot tomar sus propias decisiones. Desde este punto de vista, el robot no se considerará más como un esclavo, sino como un compañero. El sistema de toma de decisiones está basado en necesidades (drives), motivaciones, emociones y auto-aprendizaje. De acuerdo a diversas teorías psicológicas, las necesidades son carencias o déficits de variables internas (por ejemplo, la energía) y el impulso para corregir estas necesidades son las motivaciones (como por ejemplo la supervivencia). Considerando un enfoque homeostático, el objetivo del robot es satisfacer sus carencias manteniéndolas en un nivel aceptable. Esto quiere decir que el bienestar del robot debe ser lo más alto posible. El proceso de aprendizaje permite al robot desarrollar el comportamiento necesario según las distintas motivaciones para lograr su objetivo. En esta tesis, las emociones son consideradas de forma individual desde un punto de vista funcional. Esto significa que, considerando las diferentes funciones de las emociones en animales y humanos, cada una de las emociones artificiales juega un papel diferente. Por un lado, la felicidad y la tristeza se usan durante el aprendizaje como refuerzo o castigo respectivamente y, por tanto, evaluan el comportamiento del robot. Por otro lado, el miedo juega un papel motivacional, es decir, es considerado como una motivación la cual “empuja” el robot a evitar las situaciones peligrosas. Los detalles y las ventajas de estas emociones en un robot real se muestran empíricamente a lo largo de este libro. El robot decide sus acciones futuras en base a lo que ha aprendido en experiencias pasadas. A pesar de que el contexto actual del robot está limitado a un laboratorio, el robot social cohabita con personas en un entorno potencialmente no-determinístico. El robot está equipado con un repertorio de acciones pero, inicialmente, no sabe qué acción ejecutar ni cuando hacerlo. De echo, tiene que aprender la política de comportamiento, esto es, qué acción ejecutar en diferentes configuraciones del mundo (en cada estado) para satisfacer la necesidad relacionada con la motivación más alta. Puesto que el robot aprende en un entorno real interaccionando con distintos objetos, es necesario que este aprendizaje se realice en un tiempo aceptable. El algoritmo de aprendizaje que se utiliza es una variación del conocido Q-Learning, el Object Q-Learning. Mediante este algoritmo el robot aprende el valor de cada par estadoacción a través de interacción con el entorno. Esto significa, que aprende el valor de cada acción in cada posible estado. Cuanto más alto sea el valor, mejor es la acción en ese estado. Al inicio del proceso de aprendizaje, estos valores, llamados valores Q, pueden tener todos el mismo valor o pueden pueden tener asignados distintos valores. En el primer caso, el robot no dispone de conocimientos previos; en el segundo, el robot dispone de cierta información sobre la acción a elegir. Estos valores serán actualizados durante el aprendizaje. La emoción de miedo es especialmente estudiada en esta tesis. La forma de generarse esta emoción (el appraisal) y las reacciones al miedo resultan realmente útiles a la hora de dotar al robot con un mecanismo de supervivencia adaptable y fiable. Esta tesis presenta un robot social que utiliza un proceso particular para el aprendizaje de nuevos “liberadores” del miedo, es decir, dispone de la capacidad de identificar nuevas situaciones peligrosas. Además, mediante el sistema de toma de decisiones, el robot aprende diferente reacciones para protegerse ante posibles daños causados por diversos eventos impredecibles. De echo, estas reacciones al miedo son bastante similares a las reacciones al miedo que se pueden observar en la naturaleza. Otro reto importante es el diseño de la solución: el sistema de toma de decisiones tiene que diseñarse de forma que sea suficientemente flexible para permitir cambiar fácilmente la configuración o incluso para aplicarse a distintos robots. Teniendo en cuenta el enfoque bioinspirado de este trabajo, esta investigación (y muchos otros trabajos relacionados) surge como un intento de entender un poco más lo que sucede en el cerebro. El autor espera que esta tesis pueda ayudar en el estudio de los procesos mentales, en particular aquellos que pueden llevar a desórdenes mentales o cognitivos

Affective Motivational Collaboration Theory

Existing computational theories of collaboration explain some of the important concepts underlying collaboration, e.g., the collaborators\u27 commitments and communication. However, the underlying processes required to dynamically maintain the elements of the collaboration structure are largely unexplained. Our main insight is that in many collaborative situations acknowledging or ignoring a collaborator\u27s affective state can facilitate or impede the progress of the collaboration. This implies that collaborative agents need to employ affect-related processes that (1) use the collaboration structure to evaluate the status of the collaboration, and (2) influence the collaboration structure when required. This thesis develops a new affect-driven computational framework to achieve these objectives and thus empower agents to be better collaborators. Contributions of this thesis are: (1) Affective Motivational Collaboration (AMC) theory, which incorporates appraisal processes into SharedPlans theory. (2) New computational appraisal algorithms based on collaboration structure. (3) Algorithms such as goal management, that use the output of appraisal to maintain collaboration structures. (4) Implementation of a computational system based on AMC theory. (5) Evaluation of AMC theory via two user studies to a) validate our appraisal algorithms, and b) investigate the overall functionality of our framework within an end-to-end system with a human and a robot