Human-robot interaction in groups: Methodological and research practices

Understanding the behavioral dynamics that underline human-robot interactions in groups remains one of the core challenges in social robotics research. However, despite a growing interest in this topic, there is still a lack of established and validated measures that allow researchers to analyze human-robot interactions in group scenarios; and very few that have been developed and tested specifically for research conducted in the wild. This is a problem because it hinders the development of general models of human-robot interaction, and makes the comprehension of the inner workings of the relational dynamics between humans and robots, in group contexts, significantly more difficult. In this paper, we aim to provide a reflection on the current state of research on human-robot interaction in small groups, as well as to outline directions for future research with an emphasis on methodological and transversal issues.info:eu-repo/semantics/publishedVersio

The Assistant Personal Robot Project: From the APR-01 to the APR-02 Mobile Robot Prototypes

This paper describes the evolution of the Assistant Personal Robot (APR) project developed at the Robotics Laboratory of the University of Lleida, Spain. This paper describes the first APR-01 prototype developed, the basic hardware improvement, the specific anthropomorphic improvements, and the preference surveys conducted with engineering students from the same university in order to maximize the perceived affinity with the final APR-02 mobile robot prototype. The anthropomorphic improvements have covered the design of the arms, the implementation of the arm and symbolic hand, the selection of a face for the mobile robot, the selection of a neutral facial expression, the selection of an animation for the mouth, the application of proximity feedback, the application of gaze feedback, the use of arm gestures, the selection of the motion planning strategy, and the selection of the nominal translational velocity. The final conclusion is that the development of preference surveys during the implementation of the APR-02 prototype has greatly influenced its evolution and has contributed to increase the perceived affinity and social acceptability of the prototype, which is now ready to develop assistance applications in dynamic workspaces.This research was partially funded by the Accessibility Chair promoted by Indra, Adecco Foundation and the University of Lleida Foundation from 2006 to 2018. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results

또 다른 인간의 동반자: 동물의 권리를 로봇에게도?

학위논문 (석사) -- 서울대학교 대학원 : 국제대학원 국제학과(국제협력전공), 2020. 8. Jiyeoun Song.This paper considers the academic debate on and different responses to the emergence of lifelike social robots as others from humans in society. The philosophical issues surrounding legal rights that are raised by this regulatory issue will be analyzed by deploying a 2x2 matrix based on two modalities: can and should social robots have rights? On these two questions, this thesis examines how the legal treatment of animals, the original others, has evolved historically, and how the animal-robot analogy, which encourages an understanding of social robots as analogues of animals, has risen to prominence as a line of argument to push for the extension of legal rights to protect social robots akin to animals. Using the same modalities, other positions on robot rights will be examined to suggest that the debate on robot rights shows parallels to the debate on animal rights and can be modeled along similar lines. In doing so, this thesis provides an overview of the current rights debate and suggests that the robot rights debate may follow a similar trajectory to the animal rights debate in the future.I. INTRODUCTION 1 II. LITERATURE REVIEW 8 II.1. ANALYSIS 9 II.1.1. On Social Robots 9 II.1.2. On Anthropomorphism 12 II.1.3. On the Comparison between Animal and Robot Rights 14 II.2. LIMITATIONS 16 III. METHODOLOGY 18 IV. DEFINING SOCIAL ROBOTS: WHY DO WE TALK ABOUT THEM? 22 IV.1. BACKGROUND 22 IV.2. EXAMPLES OF SOCIAL ROBOTS 25 IV.3. ANTHROPOMORPHISM AS INTENTIONAL DESIGN CHOICE 27 V. THE ANIMAL RIGHTS DEBATE 34 V.1. BACKGROUND 34 V.2. DEBATE ANALYSIS: FROM INDIFFERENCE TO ADVOCACY 35 V.3. CONTEMPORARY PERSPECTIVE 44 VI. THE ROBOT RIGHTS DEBATE 49 VI.1. BACKGROUND 49 VI.2. DEBATE ANALYSIS: FROM TOOLS TO SOCIAL ENTITIES 51 VI.2.1. Q1: Since social robots cannot have rights, they should not have rights. 52 VI.2.2. Q2: Even though social robots cannot have rights, they should have rights. 55 VI.2.3. Q3: Even though social robots can have rights, they should not have rights. 59 VI.2.4. Q4: Since social robots can have rights, they should have rights. 62 VI.2.5. The Dynamics of The Discourse 64 VI.3. THE ANIMAL-ROBOT ANALOGY 73 VI.4. CONTEMPORARY PERSPECTIVE 86 VII. CONCLUSION 89 VIII. REFERENCES 92Maste

Real-time generation and adaptation of social companion robot behaviors

Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

東北大学電気通信研究所研究活動報告 第29号(2022年度)

紀要類（bulletin）departmental bulletin pape

UNOBTRUSIVE Technique Based On Infrared Thermal Imaging For Emotion Recognition In Children- With-asd- Robot Interaction

Emoções são relevantes para as relações sociais, e indivíduos com Transtorno do Espectro Autista (TEA) possuem compreensão e expressão de emoções prejudicadas. Esta tese consiste em estudos sobre a análise de emoções em crianças com desenvolvimento típico e crianças com TEA (idade entre 7 e 12 anos), por meio do imageamento térmico infravermelho (ITIV), uma técnica segura e não obtrusiva (isenta de contato), usada para registrar variações de temperatura em regiões de interesse (RIs) da face, tais como testa, nariz, bochechas, queixo e regiões periorbital e perinasal. Um robô social chamado N-MARIA (Novo-Robô Autônomo Móvel para Interação com Autistas) foi usado como estímulo emocional e mediador de tarefas sociais e pedagógicas. O primeiro estudo avaliou a variação térmica facial para cinco emoções (alegria, tristeza, medo, nojo e surpresa), desencadeadas por estímulos audiovisuais afetivos, em crianças com desenvolvimento típico. O segundo estudo avaliou a variação térmica facial para três emoções (alegria, surpresa e medo), desencadeadas pelo robô social N-MARIA, em crianças com desenvolvimento típico. No terceiro estudo, duas sessões foram realizadas com crianças com TEA, nas quais tarefas sociais e pedagógicas foram avaliadas tendo o robô N-MARIA como ferramenta e mediador da interação com as crianças. Uma análise emocional por variação térmica da face foi possível na segunda sessão, na qual o robô foi o estímulo para desencadear alegria, surpresa ou medo. Além disso, profissionais (professores, terapeuta ocupacional e psicóloga) avaliaram a usabilidade do robô social. Em geral, os resultados mostraram que o ITIV foi uma técnica eficiente para avaliar as emoções por meio de variações térmicas. No primeiro estudo, predominantes decréscimos térmicos foram observados na maioria das RIs, com as maiores variações de emissividade induzidas pelo nojo, felicidade e surpresa, e uma precisão maior que 85% para a classificação das cinco emoções. No segundo estudo, as maiores probabilidades de emoções detectadas pelo sistema de classificação foram para surpresa e alegria, e um aumento significativo de temperatura foi predominante no queixo e nariz. O terceiro estudo realizado com crianças com TEA encontrou aumentos térmicos significativos em todas as RIs e uma classificação com a maior probabilidade para surpresa. N-MARIA foi um estímulo promissor capaz de desencadear emoções positivas em crianças. A interação criança-com-TEA-e-robô foi positiva, com habilidades sociais e tarefas pedagógicas desempenhadas com sucesso pelas crianças. Além disso, a usabilidade do robô avaliada por profissionais alcançou pontuação satisfatória, indicando a N-MARIA como uma potencial ferramenta para terapias

Using artificial intelligence for pattern recognition in a sports context

Optimizing athlete’s performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.info:eu-repo/semantics/publishedVersio

The Death of the AI Author

Much of the recent literature on AI and authorship asks whether an increasing sophistication and independence of generative code should cause us to rethink embedded assumptions about the meaning of authorship. It is often suggested that recognizing the authored — and so copyrightable — nature of AI-generated works may require a less profound doctrinal leap than has historically been assumed. In this essay, we argue that the threshold for authorship does not depend on the evolution or state of the art in AI or robotics. Rather, the very notion of AI-authorship rests on a category mistake: it is an error about the ontology of authorship. Building on the established critique of the romantic author, we contend that the death of the romantic author also and equally entails the death of the AI author. Claims of AI authorship depend on a romanticized conception of both authorship and AI, and simply do not make sense in terms of the realities of the world in which the problem exists. Those realities should push us past bare doctrinal or utilitarian considerations about what an author must do. Instead, they demand an ontological consideration of what an author must be. Drawing on insights from literary and political theory, we offer an account of authorship that is fundamentally relational: authorship is a dialogic and communicative act that is inherently social, with the cultivation of selfhood and social relations being the entire point of the practice. This discussion reorientates debates about copyright’s subsistence in AI-generated works; but it also transcends copyright law, going to the normative core of how law should — and should not — think about robots and AI, and their role in human relations