A Study of Non-Linguistic Utterances for Social Human-Robot Interaction

The world of animation has painted an inspiring image of what the robots of the future could be. Taking the robots R2D2 and C3PO from the Star Wars films as representative examples, these robots are portrayed as being more than just machines, rather, they are presented as intelligent and capable social peers, exhibiting many of the traits that people have also. These robots have the ability to interact with people, understand us, and even relate to us in very personal ways through a wide repertoire of social cues. As robotic technologies continue to make their way into society at large, there is a growing trend toward making social robots. The field of Human-Robot Interaction concerns itself with studying, developing and realising these socially capable machines, equipping them with a very rich variety of capabilities that allow them to interact with people in natural and intuitive ways, ranging from the use of natural language, body language and facial gestures, to more unique ways such as expression through colours and abstract sounds. This thesis studies the use of abstract, expressive sounds, like those used iconically by the robot R2D2. These are termed Non-Linguistic Utterances (NLUs) and are a means of communication which has a rich history in film and animation. However, very little is understood about how such expressive sounds may be utilised by social robots, and how people respond to these. This work presents a series of experiments aimed at understanding how NLUs can be utilised by a social robot in order to convey affective meaning to people both young and old, and what factors impact on the production and perception of NLUs. Firstly, it is shown that not all robots should use NLUs. The morphology of the robot matters. People perceive NLUs differently across different robots, and not always in a desired manner. Next it is shown that people readily project affective meaning onto NLUs though not in a coherent manner. Furthermore, people's affective inferences are not subtle, rather they are drawn to well established, basic affect prototypes. Moreover, it is shown that the valence of the situation in which an NLU is made, overrides the initial valence of the NLU itself: situational context biases how people perceive utterances made by a robot, and through this, coherence between people in their affective inferences is found to increase. Finally, it is uncovered that NLUs are best not used as a replacement to natural language (as they are by R2D2), rather, people show a preference for them being used alongside natural language where they can play a supportive role by providing essential social cues

People Interpret Robotic Non-linguistic Utterances Categorically

We present results of an experiment probing whether adults exhibit categorical perception when affectively rating robot-like sounds (Non-linguistic Utterances). The experimental design followed the traditional methodology from the psychology domain for measuring categorical perception: stimulus continua for robot sounds were presented to subjects, who were asked to complete a discrimination and an identification task. In the former subjects were asked to rate whether stimulus pairs were affectively different, while in the latter they were asked to rate single stimuli affectively. The experiment confirms that Non-linguistic Utterances can convey affect and that they are drawn towards prototypical emotions, confirming that people show categorical perception at a level of inferred affective meaning when hearing robot-like sounds. We speculate on how these insights can be used to automatically design and generate affect-laden robot-like utterances

NON-VERBAL COMMUNICATION WITH PHYSIOLOGICAL SENSORS. THE AESTHETIC DOMAIN OF WEARABLES AND NEURAL NETWORKS

Historically, communication implies the transfer of information between bodies, yet this phenomenon is constantly adapting to new technological and cultural standards. In a digital context, it’s commonplace to envision systems that revolve around verbal modalities. However, behavioural analysis grounded in psychology research calls attention to the emotional information disclosed by non-verbal social cues, in particular, actions that are involuntary. This notion has circulated heavily into various interdisciplinary computing research fields, from which multiple studies have arisen, correlating non-verbal activity to socio-affective inferences. These are often derived from some form of motion capture and other wearable sensors, measuring the ‘invisible’ bioelectrical changes that occur from inside the body. This thesis proposes a motivation and methodology for using physiological sensory data as an expressive resource for technology-mediated interactions. Initialised from a thorough discussion on state-of-the-art technologies and established design principles regarding this topic, then applied to a novel approach alongside a selection of practice works to compliment this. We advocate for aesthetic experience, experimenting with abstract representations. Atypically from prevailing Affective Computing systems, the intention is not to infer or classify emotion but rather to create new opportunities for rich gestural exchange, unconfined to the verbal domain. Given the preliminary proposition of non-representation, we justify a correspondence with modern Machine Learning and multimedia interaction strategies, applying an iterative, human-centred approach to improve personalisation without the compromising emotional potential of bodily gesture. Where related studies in the past have successfully provoked strong design concepts through innovative fabrications, these are typically limited to simple linear, one-to-one mappings and often neglect multi-user environments; we foresee a vast potential. In our use cases, we adopt neural network architectures to generate highly granular biofeedback from low-dimensional input data. We present the following proof-of-concepts: Breathing Correspondence, a wearable biofeedback system inspired by Somaesthetic design principles; Latent Steps, a real-time auto-encoder to represent bodily experiences from sensor data, designed for dance performance; and Anti-Social Distancing Ensemble, an installation for public space interventions, analysing physical distance to generate a collective soundscape. Key findings are extracted from the individual reports to formulate an extensive technical and theoretical framework around this topic. The projects first aim to embrace some alternative perspectives already established within Affective Computing research. From here, these concepts evolve deeper, bridging theories from contemporary creative and technical practices with the advancement of biomedical technologies.Historicamente, os processos de comunicação implicam a transferência de informação entre organismos, mas este fenómeno está constantemente a adaptar-se a novos padrões tecnológicos e culturais. Num contexto digital, é comum encontrar sistemas que giram em torno de modalidades verbais. Contudo, a análise comportamental fundamentada na investigação psicológica chama a atenção para a informação emocional revelada por sinais sociais não verbais, em particular, acções que são involuntárias. Esta noção circulou fortemente em vários campos interdisciplinares de investigação na área das ciências da computação, dos quais surgiram múltiplos estudos, correlacionando a actividade nãoverbal com inferências sócio-afectivas. Estes são frequentemente derivados de alguma forma de captura de movimento e sensores “wearable”, medindo as alterações bioeléctricas “invisíveis” que ocorrem no interior do corpo. Nesta tese, propomos uma motivação e metodologia para a utilização de dados sensoriais fisiológicos como um recurso expressivo para interacções mediadas pela tecnologia. Iniciada a partir de uma discussão aprofundada sobre tecnologias de ponta e princípios de concepção estabelecidos relativamente a este tópico, depois aplicada a uma nova abordagem, juntamente com uma selecção de trabalhos práticos, para complementar esta. Defendemos a experiência estética, experimentando com representações abstractas. Contrariamente aos sistemas de Computação Afectiva predominantes, a intenção não é inferir ou classificar a emoção, mas sim criar novas oportunidades para uma rica troca gestual, não confinada ao domínio verbal. Dada a proposta preliminar de não representação, justificamos uma correspondência com estratégias modernas de Machine Learning e interacção multimédia, aplicando uma abordagem iterativa e centrada no ser humano para melhorar a personalização sem o potencial emocional comprometedor do gesto corporal. Nos casos em que estudos anteriores demonstraram com sucesso conceitos de design fortes através de fabricações inovadoras, estes limitam-se tipicamente a simples mapeamentos lineares, um-para-um, e muitas vezes negligenciam ambientes multi-utilizadores; com este trabalho, prevemos um potencial alargado. Nos nossos casos de utilização, adoptamos arquitecturas de redes neurais para gerar biofeedback altamente granular a partir de dados de entrada de baixa dimensão. Apresentamos as seguintes provas de conceitos: Breathing Correspondence, um sistema de biofeedback wearable inspirado nos princípios de design somaestético; Latent Steps, um modelo autoencoder em tempo real para representar experiências corporais a partir de dados de sensores, concebido para desempenho de dança; e Anti-Social Distancing Ensemble, uma instalação para intervenções no espaço público, analisando a distância física para gerar uma paisagem sonora colectiva. Os principais resultados são extraídos dos relatórios individuais, para formular um quadro técnico e teórico alargado para expandir sobre este tópico. Os projectos têm como primeiro objectivo abraçar algumas perspectivas alternativas às que já estão estabelecidas no âmbito da investigação da Computação Afectiva. A partir daqui, estes conceitos evoluem mais profundamente, fazendo a ponte entre as teorias das práticas criativas e técnicas contemporâneas com o avanço das tecnologias biomédicas

Using the affectbutton to measure affect in child and adult-robot interaction

This report presents data which shows how the AffectButton, a visual tool to report affect, can be used reliably by both adults and children (6-7 y.). Users were asked to identify affective labels, such as scared or surprised, on the AffectButton. We report a high inter-rater reliability between adults, between children and between adults and children. Children have the same high performance when using the AffectButton as adults, making the AffectButton a intuitive and reliable tool for letting a wide range of ages report affect