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

Expressing Robot Personality through Talking Body Language

Social robots must master the nuances of human communication as a mean to convey an effective message and generate trust. It is well-known that non-verbal cues are very important in human interactions, and therefore a social robot should produce a body language coherent with its discourse. In this work, we report on a system that endows a humanoid robot with the ability to adapt its body language according to the sentiment of its speech. A combination of talking beat gestures with emotional cues such as eye lightings, body posture of voice intonation and volume permits a rich variety of behaviors. The developed approach is not purely reactive, and it easily allows to assign a kind of personality to the robot. We present several videos with the robot in two different scenarios, and showing discrete and histrionic personalities.This work has been partially supported by the Basque Government (IT900-16 and Elkartek 2018/00114), the Spanish Ministry of Economy and Competitiveness (RTI 2018-093337-B-100, MINECO/FEDER, EU)

Proceedings of the Workshop on NLG for Human–Robot Interaction

Foster ME, Buschmeier H, Dimitra G, eds. Proceedings of the Workshop on NLG for Human–Robot Interaction. Stroudsburg, PA, USA: Association for Computational Linguistics; 2018

Toward Machine Learning-Enabled Adaptivity for Humorous Robots

Social robots benefit from a sense of humor, which requires the ability to recognize and adapt to human responses during playful interactions. Past work on humorous robots has classified audience responses with audio-based and preliminary visual-based methods following the joke punchline. Building on this progress, we conducted a survey of 20 human comedians to inform the design of robotic comedians. Using the comedians’ insights, we designed an audio-based machine learning pipeline to detect joke enjoyment while the joke setup is being read as well as following the punchline. Furthermore, we used the Facial Action Coding System as the basis for a machine learning pipeline to detect joke enjoyment using participant faces in one-on-one interactions. Finally, we designed and evaluated a new robotic comedian system using the visual classifier. In this work, we present the comedian survey findings, joke enjoyment classification methods, and insights from the deployment of our robotic comedian. This work’s contributions to humor as a robotic social skill support the likability and acceptance of social robots

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Emotional recognition in computing

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University 8/4/2010.Emotions are fundamental to human lives and decision-making. Understanding and expression of emotional feeling between people forms an intricate web. This complex interactional phenomena, is a hot topic for research, as new techniques such as brain imaging give us insights about how emotions are tied to human functions. Communication of emotions is mixed with communication of other types of information (such as factual details) and emotions can be consciously or unconsciously displayed. Affective computer systems, using sensors for emotion recognition and able to make emotive responses are under development. The increased potential for emotional interaction with products and services, in many domains, is generating much interest. Emotionally enhanced systems have potential to improve human computer interaction and so to improve how systems are used and what they can deliver. They may also have adverse implications such as creating systems capable of emotional manipulation of users. Affective systems are in their infancy and lack human complexity and capability. This makes it difficult to assess whether human interaction with such systems will actually prove beneficial or desirable to users. By using experimental design, a Wizard of Oz methodology and a game that appeared to respond to the user’s emotional signals with human-like capability, I tested user experience and reactions to a system that appeared affective. To assess users’ behaviour, I developed a novel affective behaviour coding system called ‘affectemes’. I found significant gains in user satisfaction and performance when using an affective system. Those believing the system responded to emotional signals blinked more frequently. If the machine failed to respond to their emotional signals, they increased their efforts to convey emotion, which might be an attempt to ‘repair’ the interaction. This work highlights how very complex and difficult it is to design and evaluate affective systems. I identify many issues for future work, including the unconscious nature of emotions and how they are recognised and displayed with affective systems; issues about the power of emotionally interactive systems and their evaluation; and critical ethical issues. These are important considerations for future design of systems that use emotion recognition in computing.EPSRC project grant (R81374/01

Emotional recognition in computing

Emotions are fundamental to human lives and decision-making. Understanding and expression of emotional feeling between people forms an intricate web. This complex interactional phenomena, is a hot topic for research, as new techniques such as brain imaging give us insights about how emotions are tied to human functions. Communication of emotions is mixed with communication of other types of information (such as factual details) and emotions can be consciously or unconsciously displayed. Affective computer systems, using sensors for emotion recognition and able to make emotive responses are under development. The increased potential for emotional interaction with products and services, in many domains, is generating much interest. Emotionally enhanced systems have potential to improve human computer interaction and so to improve how systems are used and what they can deliver. They may also have adverse implications such as creating systems capable of emotional manipulation of users. Affective systems are in their infancy and lack human complexity and capability. This makes it difficult to assess whether human interaction with such systems will actually prove beneficial or desirable to users. By using experimental design, a Wizard of Oz methodology and a game that appeared to respond to the user's emotional signals with human-like capability, I tested user experience and reactions to a system that appeared affective. To assess users' behaviour, I developed a novel affective behaviour coding system called 'affectemes'. I found significant gains in user satisfaction and performance when using an affective system. Those believing the system responded to emotional signals blinked more frequently. If the machine failed to respond to their emotional signals, they increased their efforts to convey emotion, which might be an attempt to 'repair' the interaction. This work highlights how very complex and difficult it is to design and evaluate affective systems. I identify many issues for future work, including the unconscious nature of emotions and how they are recognised and displayed with affective systems; issues about the power of emotionally interactive systems and their evaluation; and critical ethical issues. These are important considerations for future design of systems that use emotion recognition in computing.EThOS - Electronic Theses Online ServiceEPSRC project grant (R81374/01)GBUnited Kingdo