An Exploratory Study to Determine the Effects Conversational Repetition Has on Perceived Workload and User Experience Quality in an Online Human-Robot Interaction

Human-robot interaction studies in the Caribbean currently face two challenges. First, the robots used in these studies have difficulty understanding many of the regional accents spoken study participants. Secondly, the global pandemic has made in-person HRI studies in the Caribbean more challenging due to the physical and social distancing mandates. This paper reports on our exploratory study to determine what kind of impact these two challenges have on HRI by evaluating the effect conversational repetition has on a human-robot conversation done using video conferencing software. Using network analysis, the results obtained suggest that conversational repetition has several subtle relationships on perceived workload. One interesting finding is that frustration and effort are indirectly affected by conversational repetition. Results from the short User Experience Questionnaire indicate that the overall quality of the user experience is perceived as positive-neutral. This encouraging result indicates that video conferencing may be a suitable interaction modality for HRI studies in the Caribbean

Robots facilitate human language production

Despite recent developments in integrating autonomous and human-like robots into many aspects of everyday life, social interactions with robots are still a challenge. Here, we focus on a central tool for social interaction: verbal communication. We assess the extent to which humans co-represent (simulate and predict) a robot’s verbal actions. During a joint picture naming task, participants took turns in naming objects together with a social robot (Pepper, Softbank Robotics). Previous findings using this task with human partners revealed internal simulations on behalf of the partner down to the level of selecting words from the mental lexicon, reflected in partner-elicited inhibitory effects on subsequent naming. Here, with the robot, the partner-elicited inhibitory effects were not observed. Instead, naming was facilitated, as revealed by faster naming of word categories co-named with the robot. This facilitation suggests that robots, unlike humans, are not simulated down to the level of lexical selection. Instead, a robot’s speaking appears to be simulated at the initial level of language production where the meaning of the verbal message is generated, resulting in facilitated language production due to conceptual priming. We conclude that robots facilitate core conceptualization processes when humans transform thoughts to language during speaking.Peer Reviewe

Motivational Theory of Human Robot Teamwork

This paper presents a theory that allows us to better understand motivation in human‒robot teamwork. Teamwork with robots often involves both physical and mental activities. This implies that motivation might be particularly important to the success of human robot teams. Unfortunately, there is much we do not know with regards to the role of motivation in effective teamwork with robots. In this paper we propose the “Motivational Theory of Human‒Robot Teamwork” to better understand teamwork in human‒robot teams. In doing so, we leverage the research on robot personality.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145157/1/Motivation and Personality (2 cols) July 19 2018.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145157/4/Robert 2018.pdfDescription of Motivation and Personality (2 cols) July 19 2018.pdf : Preprint ArticleDescription of Robert 2018.pdf : Published Versio

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

A Review of Personality in Human Robot Interactions

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.Comment: 70 pages, 2 figure

A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction

Human personality has been identified as a predictor of robot acceptance in the human robot interaction (HRI) literature. Despite this, the HRI literature has provided mixed support for this assertion. To better understand the relationship between human personality and robot acceptance, this paper conducts a meta-analysis of 26 studies. Results found a positive relationship between human personality and robot acceptance. However, this relationship varied greatly by the specific personality trait along with the study sample’s age, gender diversity, task, and global region. This meta-analysis also identified gaps in the literature. Namely, additional studies are needed that investigate both the big five personality traits and other personality traits, examine a more diverse age range, and utilize samples from previously unexamined regions of the globe.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/165339/1/Esterwood et al. 2021 (one column).pdfDescription of Esterwood et al. 2021 (one column).pdf : Preprint one column versionSEL

What Do We See in Them? Identifying Dimensions of Partner Models for Speech Interfaces Using a Psycholexical Approach

Perceptions of system competence and communicative ability, termed partner models, play a significant role in speech interface interaction. Yet we do not know what the core dimensions of this concept are. Taking a psycholexical approach, our paper is the first to identify the key dimensions that define partner models in speech agent interaction. Through a repertory grid study (N=21), a review of key subjective questionnaires, an expert review of resulting word pairs and an online study of 356 users of speech interfaces, we identify three key dimensions that make up a users’ partner model: 1) perceptions towards partner competence and dependability; 2) assessment of human-likeness; and 3) a system’s perceived cognitive flexibility. We discuss the implications for partner modelling as a concept, emphasising the importance of salience and the dynamic nature of these perceptions

Gender differences in navigation dialogues with computer systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Gender is among the most influential of the factors underlying differences in spatial abilities, human communication and interactions with and through computers. Past research has offered important insights into gender differences in navigation and language use. Yet, given the multidimensionality of these domains, many issues remain contentious while others unexplored. Moreover, having been derived from non-interactive, and often artificial, studies, the generalisability of this research to interactive contexts of use, particularly in the practical domain of Human-Computer Interaction (HCI), may be problematic. At the same time, little is known about how gender strategies, behaviours and preferences interact with the features of technology in various domains of HCI, including collaborative systems and systems with natural language interfaces. Targeting these knowledge gaps, the thesis aims to address the central question of how gender differences emerge and operate in spatial navigation dialogues with computer systems. To this end, an empirical study is undertaken, in which, mixed-gender and same-gender pairs communicate to complete an urban navigation task, with one of the participants being under the impression that he/she interacts with a robot. Performance and dialogue data were collected using a custom system that supported synchronous navigation and communication between the user and the robot. Based on this empirical data, the thesis describes the key role of the interaction of gender in navigation performance and communication processes, which outweighed the effect of individual gender, moderating gender differences and reversing predicted patterns of performance and language use. This thesis has produced several contributions; theoretical, methodological and practical. From a theoretical perspective, it offers novel findings in gender differences in navigation and communication. The methodological contribution concerns the successful application of dialogue as a naturalistic, and yet experimentally sound, research paradigm to study gender and spatial language. The practical contributions include concrete design guidelines for natural language systems and implications for the development of gender-neutral interfaces in specific domains of HCI

Phonetic accommodation of human interlocutors in the context of human-computer interaction

Phonetic accommodation refers to the phenomenon that interlocutors adapt their way of speaking to each other within an interaction. This can have a positive influence on the communication quality. As we increasingly use spoken language to interact with computers these days, the phenomenon of phonetic accommodation is also investigated in the context of human-computer interaction: on the one hand, to find out whether speakers adapt to a computer agent in a similar way as they do to a human interlocutor, on the other hand, to implement accommodation behavior in spoken dialog systems and explore how this affects their users. To date, the focus has been mainly on the global acoustic-prosodic level. The present work demonstrates that speakers interacting with a computer agent also identify locally anchored phonetic phenomena such as segmental allophonic variation and local prosodic features as accommodation targets and converge on them. To this end, we conducted two experiments. First, we applied the shadowing method, where the participants repeated short sentences from natural and synthetic model speakers. In the second experiment, we used the Wizard-of-Oz method, in which an intelligent spoken dialog system is simulated, to enable a dynamic exchange between the participants and a computer agent — the virtual language learning tutor Mirabella. The target language of our experiments was German. Phonetic convergence occurred in both experiments when natural voices were used as well as when synthetic voices were used as stimuli. Moreover, both native and non-native speakers of the target language converged to Mirabella. Thus, accommodation could be relevant, for example, in the context of computer-assisted language learning. Individual variation in accommodation behavior can be attributed in part to speaker-specific characteristics, one of which is assumed to be the personality structure. We included the Big Five personality traits as well as the concept of mental boundaries in the analysis of our data. Different personality traits influenced accommodation to different types of phonetic features. Mental boundaries have not been studied before in the context of phonetic accommodation. We created a validated German adaptation of a questionnaire that assesses the strength of mental boundaries. The latter can be used in future studies involving mental boundaries in native speakers of German.Bei phonetischer Akkommodation handelt es sich um das Phänomen, dass Gesprächspartner ihre Sprechweise innerhalb einer Interaktion aneinander anpassen. Dies kann die Qualität der Kommunikation positiv beeinflussen. Da wir heutzutage immer öfter mittels gesprochener Sprache mit Computern interagieren, wird das Phänomen der phonetischen Akkommodation auch im Kontext der Mensch-Computer-Interaktion untersucht: zum einen, um herauszufinden, ob sich Sprecher an einen Computeragenten in ähnlicher Weise anpassen wie an einen menschlichen Gesprächspartner, zum anderen, um das Akkommodationsverhalten in Sprachdialogsysteme zu implementieren und zu erforschen, wie dieses auf ihre Benutzer wirkt. Bislang lag der Fokus dabei hauptsächlich auf der globalen akustisch-prosodischen Ebene. Die vorliegende Arbeit zeigt, dass Sprecher in Interaktion mit einem Computeragenten auch lokal verankerte phonetische Phänomene wie segmentale allophone Variation und lokale prosodische Merkmale als Akkommodationsziele identifizieren und in Bezug auf diese konvergieren. Dabei wendeten wir in einem ersten Experiment die Shadowing-Methode an, bei der die Teilnehmer kurze Sätze von natürlichen und synthetischen Modellsprechern wiederholten. In einem zweiten Experiment ermöglichten wir mit der Wizard-of-Oz-Methode, bei der ein intelligentes Sprachdialogsystem simuliert wird, einen dynamischen Austausch zwischen den Teilnehmern und einem Computeragenten — der virtuellen Sprachlerntutorin Mirabella. Die Zielsprache unserer Experimente war Deutsch. Phonetische Konvergenz trat in beiden Experimenten sowohl bei Verwendung natürlicher Stimmen als auch bei Verwendung synthetischer Stimmen als Stimuli auf. Zudem konvergierten sowohl Muttersprachler als auch Nicht-Muttersprachler der Zielsprache zu Mirabella. Somit könnte Akkommodation zum Beispiel im Kontext des computergstützten Sprachenlernens zum Tragen kommen. Individuelle Variation im Akkommodationsverhalten kann unter anderem auf sprecherspezifische Eigenschaften zurückgeführt werden. Es wird vermutet, dass zu diesen auch die Persönlichkeitsstruktur gehört. Wir bezogen die Big Five Persönlichkeitsmerkmale sowie das Konzept der mentalen Grenzen in die Analyse unserer Daten ein. Verschiedene Persönlichkeitsmerkmale beeinflussten die Akkommodation zu unterschiedlichen Typen von phonetischen Merkmalen. Die mentalen Grenzen sind im Zusammenhang mit phonetischer Akkommodation zuvor noch nicht untersucht worden. Wir erstellten eine validierte deutsche Adaptierung eines Fragebogens, der die Stärke der mentalen Grenzen erhebt. Diese kann in zukünftigen Untersuchungen mentaler Grenzen bei Muttersprachlern des Deutschen verwendet werden.Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 278805297: "Phonetische Konvergenz in der Mensch-Maschine-Kommunikation