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

From Word Play to World Play: Introducing Humor in Human-Computer Interaction

Humor is important in our life, whether it is at home, at work, or in public spaces. Smart technology is increasingly becoming part of our daily life. Can smart technology, sensors and actuators, not only be used to introduce smartness in our environments, but also to introduce amusement? So far understanding of humor has escaped algorithmic approaches. Nevertheless, humor research knowledge is available and is increasing. First philosophers, then psychologists, then linguists and AI researchers made humor topic of their research. The aim of this paper is to introduce humor research to the human-computer interaction community. In particular we look at how our digitally enhanced physical worlds, or smart environments, can facilitate humor creation

Addressing joint action challenges in HRI: Insights from psychology and philosophy

The vast expansion of research in human-robot interactions (HRI) these last decades has been accompanied by the design of increasingly skilled robots for engaging in joint actions with humans. However, these advances have encountered significant challenges to ensure fluent interactions and sustain human motivation through the different steps of joint action. After exploring current literature on joint action in HRI, leading to a more precise definition of these challenges, the present article proposes some perspectives borrowed from psychology and philosophy showing the key role of communication in human interactions. From mutual recognition between individuals to the expression of commitment and social expectations, we argue that communicative cues can facilitate coordination, prediction, and motivation in the context of joint action. The description of several notions thus suggests that some communicative capacities can be implemented in the context of joint action for HRI, leading to an integrated perspective of robotic communication.French National Research Agency (ANR) ANR-16-CE33-0017 ANR-17-EURE-0017 FrontCog ANR-10-IDEX-0001-02 PSLJuan de la Cierva-Incorporacion grant IJC2019-040199-ISpanish Government PID2019-108870GB-I00 PID2019-109764RB-I0

Building Persuasive Robots with Social Power Strategies

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social robots. The results show that robotic persuaders that exert social power (specifically from expert, reward, and coercion bases) demonstrate increased ability to influence humans. The first study provides a positive answer and shows that under the same circumstances, people with different personalities prefer robots using a specific social power base. In addition, social rewards can be useful in persuading individuals. The second study suggests that by employing social power, social robots are capable of persuading people objectively to select a less desirable choice among others. Finally, the third study shows that the effect of power on persuasion does not decay over time and might strengthen under specific circumstances. Moreover, exerting stronger social power does not necessarily lead to higher persuasion. Overall, we argue that the results of these studies are relevant for designing human--robot-interaction scenarios especially the ones aiming at behavioral change

人の行動計測によるユーザタイプを考慮したヒューマンロボットインタラクション

首都大学東

Gender and Human-Machine Communication: Where Are We?

In this introduction to the fifth volume of the journal Human-Machine Communication, we present and discuss the five articles focusing on gender and human-machine communication. In this essay, we will analyze the theme of gender, including how this notion has historically and politically been set up, and for what reasons. We will start by considering gender in in-person communication, then we will progress to consider what happens to gender when it is mediated by the most important ICTs that preceded HMC: the telephone, mobile phone, and computer-mediated communication (CMC). We outline the historical framework necessary to analyze the last section of the essay, which focuses on gender in HMC. In the conclusion, we will set up some final sociological and political reflections on the social meaning of these technologies for gender and specifically for women

Collaboration and competition in groups of humans and robots: effects on socioemotional and task-oriented behaviors

Advancements in technology have allowed the emergence of novel forms of social interaction. More specifically, in the last decades, the emergence of social robots has triggered a multidisciplinary effort towards achieving a better understanding of how humans and robots interact. In this dissertation, our goal was to contribute towards that effort by considering the role of goal orientation displayed by the robot (i.e. competitive vs. cooperative) and the role displayed by each player (partners and opponents). Sixty participants engaged in a typical Portuguese card-game called Sueca (two robots and two humans). Each participant played three games with each of the other players and the goal orientation was manipulated by the set of pre-validated verbal utterances displayed by the robot. The interactions were video-recorded, and we used a coding scheme based on Bales Interaction Process Analysis (1950) for small groups to analyze socioemotional positive, negative and task-oriented behaviors. A MultiLevel Modelling analysis yielded a significant effect of the role for all dimensions. Participants directed more socioemotional positive and task-oriented behaviors towards the human playing as a partner than as opponent and also interacted more with the other human in comparison to both robots. Comparing both robots, participants displayed more positive and task-oriented behaviors when interacting with robots as opponents than as partners. These results suggest the occurrence of different behavioral patterns in competitive and collaborative interactions with robots, that might be useful to inform the future development of more socially effective robots.O desenvolvimento de novas tecnologias tem proporcionado a emergência de novas formas de interação social. Mais especificamente, nas últimas décadas, o desenvolvimento de robôs sociais tem despoletado um esforço interdisciplinar orientado para o estabelecimento de uma melhor compreensão acerca da forma como pessoas e robôs interagem. Com esta dissertação, pretendemos contribuir para esse esforço considerando o efeito da orientação estratégica exibida pelo robô (i.e. competitivo vs. colaborativo) e o efeito do papel assumido pelos jogadores (parceiro ou oponente). Sessenta participantes jogaram à Sueca (dois robôs e dois humanos). Cada participante jogou três jogos em parceria com cada um dos outros jogadores e a orientação estratégica foi manipulada através do conjunto pré-validado de interações verbais exibido pelos robôs. As interações foram filmadas e analisadas usando o guião de análise sugerido por Bales (1950) que inclui interações socioemocionais negativas, positivas e relacionadas com a tarefa. Uma análise Multi-nível dos resultados revelou um efeito principal do papel para todas as dimensões. Os participantes dirigiram mais comportamentos positivos e relacionados com a tarefa para os humanos no papel de parceiros do que oponentes e interagiram mais frequentemente com o humano do que com os robôs. Os participantes também direcionaram mais interações positivas e relacionadas com a tarefa para os robôs quando estes assumiram o papel de oponentes, em comparação com quando jogaram como parceiros. Estes resultados sugerem a ocorrência de diferentes padrões comportamentais quando interagindo com robôs competitivos e colaborativos que poderão ser úteis para informar o desenvolvimento de robôs mais socialmente eficazes