Trust in Robots

Robots are increasingly becoming prevalent in our daily lives within our living or working spaces. We hope that robots will take up tedious, mundane or dirty chores and make our lives more comfortable, easy and enjoyable by providing companionship and care. However, robots may pose a threat to human privacy, safety and autonomy; therefore, it is necessary to have constant control over the developing technology to ensure the benevolent intentions and safety of autonomous systems. Building trust in (autonomous) robotic systems is thus necessary. The title of this book highlights this challenge: “Trust in robots—Trusting robots”. Herein, various notions and research areas associated with robots are unified. The theme “Trust in robots” addresses the development of technology that is trustworthy for users; “Trusting robots” focuses on building a trusting relationship with robots, furthering previous research. These themes and topics are at the core of the PhD program “Trust Robots” at TU Wien, Austria

Social Emotions in Multiagent Systems

Tesis por compendioA lo largo de los últimos años, los sistemas multi-agente (SMA) han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos. Este potencial no se debe principalmente a sus características individuales (como son su autonomía, su capacidad de percepción, reacción y de razonamiento), sino que también a la capacidad de comunicación y cooperación a la hora de conseguir un objetivo. De hecho, su capacidad social es la que más llama la atención, es este comportamiento social el que dota de potencial a los sistemas multi-agente. Estas características han hecho de los SMA, la herramienta de inteligencia artificial (IA) más utilizada para el diseño de entornos virtuales inteligentes (IVE), los cuales son herramientas de simulación compleja basadas en agentes. Sin embargo, los IVE incorporan restricciones físicas (como gravedad, fuerzas, rozamientos, etc.), así como una representación 3D de lo que se quiere simular. Así mismo, estas herramientas no son sólo utilizadas para la realización de simulaciones. Con la aparición de nuevas aplicaciones como \emph{Internet of Things (IoT)}, \emph{Ambient Intelligence (AmI)}, robot asistentes, entre otras, las cuales están en contacto directo con el ser humano. Este contacto plantea nuevos retos a la hora de interactuar con estas aplicaciones. Una nueva forma de interacción que ha despertado un especial interés, es el que se relaciona con la detección y/o simulación de estados emocionales. Esto ha permitido que estas aplicaciones no sólo puedan detectar nuestros estados emocionales, sino que puedan simular y expresar sus propias emociones mejorando así la experiencia del usuario con dichas aplicaciones. Con el fin de mejorar la experiencia humano-máquina, esta tesis plantea como objetivo principal la creación de modelos emocionales sociales, los cuales podrán ser utilizados en aplicaciones MAS permitiendo a los agentes interpretar y/o emular diferentes estados emocionales y, además, emular fenómenos de contagio emocional. Estos modelos permitirán realizar simulaciones complejas basadas en emociones y aplicaciones más realistas en dominios como IoT, AIm, SH.Over the past few years, multi-agent systems (SMA) have proven to be a powerful and versatile paradigm, with great potential for solving complex problems in dynamic and distributed environments. This potential is not primarily due to their individual characteristics (such as their autonomy, their capacity for perception, reaction and reasoning), but also the ability to communicate and cooperate in achieving a goal. In fact, its social capacity is the one that draws the most attention, it is this social behavior that gives potential to multi-agent systems. These characteristics have made the SMA, the artificial intelligence (AI) tool most used for the design of intelligent virtual environments (IVE), which are complex agent-based simulation tools. However, IVE incorporates physical constraints (such as gravity, forces, friction, etc.), as well as a 3D representation of what you want to simulate. Also, these tools are not only used for simulations. With the emergence of new applications such as \emph {Internet of Things (IoT)}, \emph {Ambient Intelligence (AmI)}, robot assistants, among others, which are in direct contact with humans. This contact poses new challenges when it comes to interacting with these applications. A new form of interaction that has aroused a special interest is that which is related to the detection and / or simulation of emotional states. This has allowed these applications not only to detect our emotional states, but also to simulate and express their own emotions, thus improving the user experience with those applications. In order to improve the human-machine experience, this thesis aims to create social emotional models, which can be used in MAS applications, allowing agents to interpret and / or emulate different emotional states, and emulate phenomena of emotional contagion. These models will allow complex simulations based on emotions and more realistic applications in domains like IoT, AIm, SH.Al llarg dels últims anys, els sistemes multi-agent (SMA) han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexos en entorns dinàmics i distribuïts. Aquest potencial no es deu principalment a les seues característiques individuals (com són la seua autonomia, la seua capacitat de percepció, reacció i de raonament), sinó que també a la capacitat de comunicació i cooperació a l'hora d'aconseguir un objectiu. De fet, la seua capacitat social és la que més crida l'atenció, és aquest comportament social el que dota de potencial als sistemes multi-agent. Aquestes característiques han fet dels SMA, l'eina d'intel·ligència artificial (IA) més utilitzada per al disseny d'entorns virtuals intel·ligents (IVE), els quals són eines de simulació complexa basades en agents. No obstant això, els IVE incorporen restriccions físiques (com gravetat, forces, fregaments, etc.), així com una representació 3D del que es vol simular. Així mateix, aquestes eines no són només utilitzades per a la realització de simulacions. Amb l'aparició de noves aplicacions com \emph{Internet of Things (IOT)}, \emph{Ambient Intelligence (AmI)}, robot assistents, entre altres, les quals estan en contacte directe amb l'ésser humà. Aquest contacte planteja nous reptes a l'hora d'interactuar amb aquestes aplicacions. Una nova forma d'interacció que ha despertat un especial interès, és el que es relaciona amb la detecció i/o simulació d'estats emocionals. Això ha permès que aquestes aplicacions no només puguen detectar els nostres estats emocionals, sinó que puguen simular i expressar les seues pròpies emocions millorant així l'experiència de l'usuari amb aquestes aplicacions. Per tal de millorar l'experiència humà-màquina, aquesta tesi planteja com a objectiu principal la creació de models emocionals socials, els quals podran ser utilitzats en aplicacions MAS permetent als agents interpretar i/o emular diferents estats emocionals i, a més, emular fenòmens de contagi emocional. Aquests models permetran realitzar simulacions complexes basades en emocions i aplicacions més realistes en dominis com IoT, AIM, SH.Rincón Arango, JA. (2018). Social Emotions in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/98090TESISCompendi

Machine Medical Ethics

In medical settings, machines are in close proximity with human beings: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. Machines in these contexts are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for empathy and emotion detection necessary? What about consciousness? The essays in this collection by researchers from both humanities and science describe various theoretical and experimental approaches to adding medical ethics to a machine, what design features are necessary in order to achieve this, philosophical and practical questions concerning justice, rights, decision-making and responsibility, and accurately modeling essential physician-machine-patient relationships. This collection is the first book to address these 21st-century concerns

Cognimates

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.Cataloged from PDF version of thesis. "Some pages in the original document contain text that runs off the edge of the page"--Disclaimer Notice page.Includes bibliographical references (pages 199-204).Conversational agents and intelligent toys are present in children's homes. This raises questions as to the impact of Al on their development. In this context, we explore how to educate the children that are growing up with Al and best prepare them for the future. Our prior studies showed that young people consider intelligent agents as friendly and trustworthy, and sometimes even defer to them when making decisions [16, 73]. This thesis explores how children, who are 7 to 14 years old, develop a better understanding of Al concepts and change their perception of smart agents by programming and teaching them with the Cognimates platform we developed. Variations between children of different nationalities and SES backgrounds are discussed together with the influence of their collaboration and communication skills.by Stefania Druga.S.M

Devobot: From Biological Morphogenesis to Morphogenetic Swarm Robotics

Complex systems are composed of a large number of relatively simple entities interacting with each other and their environment. From those entities and interactions emerge new and often unpredictable collective structures. Complex systems are widely present in nature, from cells and living organisms to human societies. A major biological process behind this emergence in natural complex systems is morphogenesis, which refers mainly, although not exclusively, to shape development in multicellular organisms. Inspired by morphogenesis, the field of Morphogenetic Engineering (ME) aims to design a system’s global architecture and behaviour in a bottom-up fashion from the self-organisation of a myriad of small components. In particular, Morphogenetic Robotics (MR) strives to apply ME to Swarm Robotics in order to create robot collectives exhibiting morphogenetic properties. While most MR works focus on small and cheap hardware, such as Kilobots, only few or them investigate swarms of mobile and more “intelligent” robot models. In this thesis, we present two original works involving higher-end MR swarms based on the PsiSwarm platform, a two-wheeled saucer-size robot running the Mbed operating system. First, we describe a novel distributed algorithm capable of growing a densely packed “multi-robot organism” out of a group of 40 PsiSwarms, based on ME principles. Then, in another study closer to Modular Robotics (MoR), and taking inspiration from “programmable network growth”, we demonstrate the self-organisation of (virtual) branched structures among a flock of robots. Both works use MORSE, a realistic simulation tool, while a path toward crossing the “reality gap” is shown by preliminary experiments conducted using real hardware

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Mission programming for flying ensembles: combining planning with self-organization

The application of autonomous mobile robots can improve many situations of our daily lives. Robots can enhance working conditions, provide innovative techniques for different research disciplines, and support rescue forces in an emergency. In particular, flying robots have already shown their potential in many use-cases when cooperating in ensembles. Exploiting this potential requires sophisticated measures for the goal-oriented, application-specific programming of flying ensembles and the coordinated execution of so defined programs. Because different goals require different robots providing different capabilities, several software approaches emerged recently that focus on specifically designed robots. These approaches often incorporate autonomous planning, scheduling, optimization, and reasoning attributable to classic artificial intelligence. This allows for the goal-oriented instruction of ensembles, but also leads to inefficiencies if ensembles grow large or face uncertainty in the environment. By leaving the detailed planning of executions to individuals and foregoing optimality and goal-orientation, the selforganization paradigm can compensate for these drawbacks by scalability and robustness. In this thesis, we combine the advantageous properties of autonomous planning with that of self-organization in an approach to Mission Programming for Flying Ensembles. Furthermore, we overcome the current way of thinking about how mobile robots should be designed. Rather than assuming fixed-design robots, we assume that robots are modifiable in terms of their hardware at run-time. While using such robots enables their application in many different use cases, it also requires new software approaches for dealing with this flexible design. The contributions of this thesis thus are threefold. First, we provide a layered reference architecture for physically reconfigurable robot ensembles. Second, we provide a solution for programming missions for ensembles consisting of such robots in a goal-oriented fashion that provides measures for instructing individual robots or entire ensembles as desired in the specific use case. Third, we provide multiple self-organization mechanisms to deal with the system’s flexible design while executing such missions. Combining different self-organization mechanisms ensures that ensembles satisfy the static requirements of missions. We provide additional self-organization mechanisms for coordinating the execution in ensembles ensuring they meet the dynamic requirements of a mission. Furthermore, we provide a solution for integrating goal-oriented swarm behavior into missions using a general pattern we have identified for trajectory-modification-based swarm behavior. Using that pattern, we can modify, quantify, and further process the emergent effect of varying swarm behavior in a mission by changing only the parameters of its implementation. We evaluate results theoretically and practically in different case studies by deploying our techniques to simulated and real hardware.Der Einsatz von autonomen mobilen Robotern kann viele Abläufe unseres täglichen Lebens erleichtern. Ihr Einsatz kann Arbeitsbedingungen verbessern, als innovative Technik für verschiedene Forschungsdisziplinen dienen oder Rettungskräfte im Einsatz unterstützen. Insbesondere Flugroboter haben ihr Potenzial bereits in vielerlei Anwendungsfällen gezeigt, gerade wenn mehrere in Ensembles eingesetzt werden. Das Potenzial fliegender Ensembles zielgerichtet und anwendungsspezifisch auszuschöpfen erfordert ausgefeilte Programmiermethoden und Koordinierungsverfahren. Zu diesem Zweck sind zuletzt viele unterschiedliche und auf speziell entwickelte Roboter zugeschnittene Softwareansätze entstanden. Diese verwenden oft klassische Planungs-, Scheduling-, Optimierungs- und Reasoningverfahren. Während dies vor allem den zielgerichteten Einsatz von Ensembles ermöglicht, ist es jedoch auch oft ineffizient, wenn die Ensembles größer oder deren Einsatzumgebungen unsicher werden. Die genannten Nachteile können durch das Paradigma der Selbstorganisation kompensiert werden: Falls Anwendungen nicht zwangsläufig auf Optimalität und strikte Zielorientierung ausgelegt sind, kann so Skalierbarkeit und Robustheit im System erreicht werden. In dieser Arbeit werden die vorteilhaften Eigenschaften klassischer Planungstechniken mit denen der Selbstorganisation in einem Ansatz zur Missionsprogrammierung für fliegende Ensembles kombiniert. In der dafür entwickelten Lösung wird von der aktuell etablierten Ansicht einer unveränderlichen Roboterkonstruktion abgewichen. Stattdessen wird die Hardwarezusammenstellung der Roboter als zur Laufzeit modifizierbar angesehen. Der Einsatz solcher Roboter erfordert neue Softwareansätze um mit genannter Flexibilität umgehen zu können. Die hier vorgestellten Beiträge zu diesem Thema lassen sich in drei Punkten zusammenfassen: Erstens wird eine Schichtenarchitektur als Referenz für physikalisch konfigurierbare Roboterensembles vorgestellt. Zweitens wird eine Lösung zur zielorientierten Missions-Programmierung für derartige Ensembles präsentiert, mit der sowohl einzelne Roboter als auch ganze Ensembles instruiert werden können. Drittens werden mehrere Selbstorganisationsmechanismen vorgestellt, die die autonome Ausführung so erstellter Missionen ermöglichen. Durch die Kombination verschiedener Selbstorganisationsmechanismen wird sichergestellt, dass Ensembles die missionsspezifischen Anforderungen erfüllen. Zusätzliche Selbstorganisationsmechanismen ermöglichen die koordinierte Ausführung der Missionen durch die Ensembles. Darüber hinaus bietet diese Lösung die Möglichkeit der Integration zielorientierten Schwarmverhaltens. Durch ein allgemeines algorithmisches Verfahren für auf Trajektorien-Modifikation basierendes Schwarmverhalten können allein durch die Änderung des Parametersatzes unterschiedliche emergente Effekte in einer Mission erzielt, quantifiziert und weiterverarbeitet werden. Zur theoretischen und praktischen Evaluierung der Ergebnisse dieser Arbeit wurden die vorgestellten Techniken in verschiedenen Fallstudien auf simulierter sowie realer Hardware zum Einsatz gebracht

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Apprentissage permanent par feedback endogène, application à un système robotique

Les applications robotiques sont liées à l'environnement sociotechnique dynamique dans lequel elles sont intégrées. Dans ce contexte, l'auto-adaptation est une préoccupation centrale et la conception d'applications intelligentes dans de tels environnements nécessite de les considérer comme des systèmes complexes. Le domaine de la robotique est très vaste. L'accent est mis sur les systèmes qui s'adaptent aux contraintes de leur environnement et non sur la mécanique ou le traitement du signal. À la lumière de ce contexte, l'objectif de cette thèse est la conception d'un mécanisme d'apprentissage capable d'apprendre de manière continue en utilisant des feedbacks endogènes (i.e. des interactions internes) dans des environnements sociotechniques dynamiques. Ce mécanisme d'apprentissage doit aussi vérifier plusieurs propriétés qui sont essentielles dans ce contexte comme : l'agnosticité, l'apprentissage tout au long de la vie, l'apprentissage en ligne, l'auto-observation, la généralisation des connaissances, le passage à l'échelle, la tolérance au volume de données et l'explicabilité. Les principales contributions consistent en la construction de l'apprentissage endogène par contextes et la conception du mécanisme d'apprentissage ELLSA pour Endogenous Lifelong Learner by Self-Adaptation. Le mécanisme d'apprentissage proposé est basé sur les systèmes multi-agents adaptatifs combinés à l'apprentissage endogène par contextes. La création de l'apprentissage endogène par contextes est motivée par la caractérisation d'imprécisions d'apprentissage qui sont détectées par des négociations locales entre agents. L'apprentissage endogène par contextes comprends aussi un mécanisme de génération de données artificielles pour améliorer les modèles d'apprentissage tout en réduisant la quantité nécessaire de données d'apprentissage. Dans un contexte d'apprentissage tout au long de la vie, ELLSA permet une mise à jour dynamique des modèles d'apprentissage. Il introduit des stratégies d'apprentissage actif et d'auto-apprentissage pour résoudre les imprécisions d'apprentissage. L'utilisation de ces stratégies dépend de la disponibilité des données d'apprentissage. Afin d'évaluer ses contributions, ce mécanisme est appliqué à l'apprentissage de fonctions mathématiques et à un problème réel dans le domaine de la robotique : le problème de la cinématique inverse. Le scénario d'application est l'apprentissage du contrôle de bras robotiques multi-articulés. Les expériences menées montrent que l'apprentissage endogène par contextes permet d'améliorer les performances d'apprentissage grâce à des mécanismes internes. Elles mettent aussi en évidence des propriétés du système selon les objectifs de la thèse : feedback endogènes, agnosticité, apprentissage tout au long de la vie, apprentissage en ligne, auto-observation, généralisation, passage à l'échelle, tolérance au volume de données et explicabilité.Robotic applications are linked to the dynamic sociotechnical environment in which they are embedded. In this scope, self-adaptation is a central concern and the design of intelligent applications in such environments requires to consider them as complex systems. The field of robotics is very broad. The focus is made on systems that adapt to the constraints of their environment and not on mechanics or signal processing. In light of this context, the objective of this thesis is the design of a learning mechanism capable of continuous learning using endogenous feedback (i.e. internal interactions) in dynamic sociotechnical environments. This learning mechanism must also verify several properties that are essential in this context such as: agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability. The main contributions consist of the construction of Endogenous Context Learning and the design of the learning mechanism ELLSA for Endogenous Lifelong Learner by Self-Adaptation. The proposed learning mechanism is based on Adaptive Multi-Agent Systems combined with Context Learning. The creation of Endogenous Context Learning is motivated by the characterization of learning inaccuracies that are detected by local negotiations between agents. Endogenous Context Learning also includes an artificial data generation mechanism to improve learning models while reducing the amount of the required learning data. In a Lifelong Learning setting, ELLSA enables dynamic updating of learning models. It introduces Active Learning and Self-Learning strategies to resolve learning inaccuracies. The use of these strategies depends on the availability of learning data. In order to evaluate its contributions, this mechanism is applied to the learning of mathematical functions and to a real problem in the field of robotics: the Inverse Kinematics problem. The application scenario is the learning of the control of multi-jointed robotic arms. The conducted experiments show that Endogenous Context Learning enables to improve the learning performances thanks to internal mechanisms. They also highlight the properties of the system according to the objectives of the thesis: endogenous feedback, agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability