Nas ciências de computação, o estudo de emoções tem sido impulsionado pela construção de ambientes interativos, especialmente no contexto dos dispositivos móveis. Pesquisas envolvendo interação humano-robô têm explorado emoções para propiciar experiências naturais de interação com robôs sociais. Um dos aspectos a serem investigados é o das abordagens práticas que exploram mudanças na personalidade de um sistema artificial propiciadas por alterações em um estado emocional inferido do usuário. Neste trabalho, é proposto um ambiente para interação humano-robô baseado em emoções, reconhecidas por meio de análise de expressões faciais, para plataforma Android. Esse sistema consistiu em um agente virtual agregado a um aplicativo, o qual usou informação proveniente de um reconhecedor de emoções para adaptar sua estratégia de interação, alternando entre dois paradigmas discretos pré-definidos. Nos experimentos realizados, verificou-se que a abordagem proposta tende a produzir mais empatia do que uma condição controle, entretanto esse resultado foi observado somente em interações suficientemente longas. / In computer sciences, the development of interactive environments have motivated the study of emotions, especially on the context of mobile devices. Research in human-robot interaction have explored emotions to create natural experiences on interaction with social robots. A fertile aspect consist on practical approaches concerning changes on the personality of an artificial system caused by modifications on the users inferred emotional state. The present project proposes to develop, for Android platform, an environment for human-robot interaction based on emotions. A dedicated module will be responsible for recognizing emotions by analyzing facial expressions. This system consisted of a virtual agent aggregated to an application, which used information of the emotion recognizer to adapt its interaction strategy, alternating between two pre-defined discrete paradigms. In the experiments performed, it was found that the proposed approach tends to produce more empathy than a control condition, however this result was observed only in sufficiently long interactions.

Communication in HRI, both verbal and non-verbal, can be hard for a robot to interpret and to convey which can lead to misinterpretations by both the human and the robot. In this thesis we look at answering the question if AR can be used to improve communication of a social robot’s intentions when interacting with children. We looked at behaviors such as getting children to pick up a cube, place a cube, give the cube to another child, tap the cube and shake the cube. We found that picking the cube was the most successful and reliable behavior and that most behaviors were slightly better with AR. Additionally, endorsement behavior was found to be necessary to engage the children, however, it needs to be quicker, more responsive and clearer. In conclusion, there is potential for using AR to improve the intent communication of a robot, but in many cases, the robot behavior alone was already quite clear. A larger study would need to be conducted to further explore this. / I Människa-Robot Interaktion kan både verbal och icke-verbal kommunikation vara svårt för en robot att förstå och förmedla vilket kan leda till missförstånd från både människans och robotens håll. I den här rapporten vill vi svara på frågan ifall AR kan användas för att förbättra kommunikationen av en social robots avsikter när den interagerar med barn. De beteenden vi kollade på var att få ett barn att plocka upp en kub, placera den, ge den till ett annat barn, knacka på den och skaka den. Resultaten var att plocka upp kuben var det mest framgångsrika och pålitliga beteendet och att de flesta beteenden var marginellt bättre med AR. Utöver det hittade vi också att bifallsbeteenden behövdes för att engagera barnen men behövde vara snabbare, mer responsiva och tydligare. Sammanfattningsvis finns det potential för att använda AR, men i många fall var enbart robotens beteenden redan väldigt tydliga. En större studie skulle behövas för att utforska detta ytterligare.

Social robots and artificial intelligence are radical innovations at the cutting edge of technology. Touted as the fourth industrial revolution, the future is looking bright for social robotics, and for the markets which can benefit from this technology. However, despite a wealth of research regarding technical functionality, there has been little research conducted into the future strategies required to ensure the successful diffusion of these innovations into society or effective methods of influencing rapid adoption rates in target markets.  The following research questions have been designed to identify potential solutions to existing and future problems facing the social robotics industry: What are the barriers to the early stages of the diffusion of innovation for social robotics start-ups? How can these innovative companies cross the chasm? In order to formulate the findings, primary research was conducted in the form of interviews within three categories: academics, practitioners and social robotics experts. Secondary research was undertaken to analyse and compare primary findings. The research is purely qualitative as quantitative data was purposefully disregarded due to limitations on time and scope.  In summary, social robotics start-ups face significant barriers to diffusion such as inherently expensive products and misaligned customer expectations. Attracting ‘pragmatists in pain’ is vital to be able to cross the chasm and a strong reference base is necessary for social robots to be adopted in the mainstream market. Start-ups need to meet the demands of the ‘expected product’ to attract the early majority (pragmatist) segment, providing a greater possibility of crossing the chasm and enabling rapid adoption. It is assumed that either a mass or niche strategy can be chosen, depending on the type of product in subject. An adaptation to the technology adoption life cycle has been made in the form of the ‘double-bell curve’ and the ‘V’ in the chasm has been identified within the process of successful diffusion. Methods of improving the rate of adoption have been applied in consideration of the ‘technology acceptance model’, with a heavy focus on increasing trialability and observability. There is a risk of potential ‘overadoption’ in the social robotics industry, however the changing shift in customer attitudes towards technology adoption lowers boundaries to diffusion.

Loneliness among older adults is a problem with severe consequences to individual health, quality of life, cognitive capacity, and life-expectancy. Although approaches towards improving the quality and quantity of social relationships are the prevailing model of therapy, older adults may not always be able to form these relationships due to either personality factors, decreased mobility, or isolation. Intelligent personal assistants (IPAs), virtual agents, and social robotics offer an opportunity for the development of technology that could potentially serve as social companions to older adults. The present study explored whether an IPA could potentially be used as a social companion to older adults feeling lonely. Additionally, the research explored whether the device has the potential to generate social presence among both young and older adults. Results indicate that while the devices do show some social presence, participants rate the device low on some components of social presence, such as emotional contagion. This adversely affects the possibility of a social relationship between an older adult and the device. Analysis reveals ways to improve social presence in these devices.

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An important capacity that is still lacking in intelligent systems such as robots, is the ability to use concepts in a human-like manner. Indeed, the use of concepts has been recognised as being fundamental to a wide range of cognitive skills, including classification, reasoning and memory. Intricately intertwined with language, concepts are at the core of human cognition; but despite a large body or research, their functioning is as of yet not well understood. Nevertheless it remains clear that if intelligent systems are to achieve a level of cognition comparable to humans, they will have to posses the ability to deal with the fundamental role that concepts play in cognition. A promising manner in which conceptual knowledge can be acquired by an intelligent system is through ongoing, incremental development. In this view, a system is situated in the world and gradually acquires skills and knowledge through interaction with its social and physical environment. Important in this regard is the notion that cognition is embodied. As such, both the physical body and the environment shape the manner in which cognition, including the learning and use of concepts, operates. Through active partaking in the interaction, an intelligent system might influence its learning experience as to be more effective. This work presents experiments which illustrate how these notions of interaction and embodiment can influence the learning process of artificial systems. It shows how an artificial agent can benefit from interactive learning. Rather than passively absorbing knowledge, the system actively partakes in its learning experience, yielding improved learning. Next, the influence of embodiment on perception is further explored in a case study concerning colour perception, which results in an alternative explanation for the question of why human colour experience is very similar amongst individuals despite physiological differences. Finally experiments, in which an artificial agent is embodied in a novel robot that is tailored for human-robot interaction, illustrate how active strategies are also beneficial in an HRI setting in which the robot learns from a human teacher.

A disseminação do uso de robôs na sociedade moderna é uma realidade. Do começo restrito às operações fabris como pintura e soldagem, até o início de seu uso nas residências, apenas algumas décadas se passaram. A robótica social é uma área de pesquisa que visa desenvolver modelos para que a interação direta de robôs com seres humanos ocorra de forma natural. Um dos fatores que compromete a rápida evolução da robótica social é a dificuldade em integrar sistemas cognitivos e robóticos, principalmente devido ao volume e complexidade da informação produzida por um mundo caótico repleto de dados sensoriais. Além disso, a existência de múltiplas configurações de robôs, com arquiteturas e interfaces distintas, dificulta a verificação e repetibilidade dos experimentos realizados pelos diversos grupos de pesquisa. Esta tese contribui para a evolução da robótica social ao definir uma arquitetura, denominada Cognitive Model Development Environment (CMDE) que simplifica a conexão entre sistemas cognitivos e robóticos. Essa conexão é formalizada com uma ontologia, denominada OntPercept, que modela a percepção do ambiente a partir de informações sensoriais captadas pelos sensores presentes no agente robótico. Nos últimos anos, diversas ontologias foram propostas para aplicações robóticas, mas elas não são genéricas o suficiente para atender completamente às necessidades das áreas de robótica e automação. A formalização oferecida pela OntPercept facilita o desenvolvimento, a reprodução e a comparação de experimentos associados a robótica social. A validação do sistema proposto ocorre com suporte do simulador Robot House Simulator (RHS), que fornece um ambiente onde, o agente robótico e o personagem humano podem interagir socialmente com níveis crescentes de processamento cognitivo. A proposta da CMDE viabiliza a utilização de qualquer sistema cognitivo, em particular, o experimento elaborado para validação desta pesquisa utiliza Soar como arquitetura cognitiva. Em conjunto, os elementos: arquitetura CMDE, ontologia OntPercept e simulador RHS, todos disponibilizados livremente no GitHub, estabelecem um ambiente completo que propiciam o desenvolvimento de experimentos envolvendo sistemas cognitivos dirigidos para a área de robótica social. / The use of robots in modern society is a reality. From the beginning restricted to the manufacturing operations like painting and welding, until the beginning of its use in the residences, only a few decades have passed. Social robotics is an area that aims to develop models so that the direct interaction of robots with humans occurs naturally. One of the factors that compromises the rapid evolution of social robotics is the difficulty in integrating cognitive and robotic systems, mainly due to the volume and complexity of the information produced by a chaotic world full of sensory data. In addition, the existence of multiple configurations of robots, with different architectures and interfaces, makes it difficult to verify and repeat the experiments performed by the different research groups. This research contributes to the evolution of social robotics by defining an architecture, called Cognitive Model Development Environment (CMDE), which simplifies the connection between cognitive and robotic systems. This connection is formalized with an ontology, called OntPercept, which models the perception of the environment from the sensory information captured by the sensors present in the robotic agent. In recent years, several ontologies have been proposed for robotic applications, but they are not generic enough to fully address the needs of robotics and automation. The formalization offered by OntPercept facilitates the development, reproduction and comparison of experiments associated with social robotics. The validation of the proposed system occurs with support of the Robot House Simulator (RHS), which provides an environment where the robotic agent and the human character can interact socially with increasing levels of cognitive processing. All together, the elements: CMDE architecture, OntPercept ontology and RHS simulator, all freely available in GitHub, establish a complete environment that allows the dev

A disseminação do uso de robôs na sociedade moderna é uma realidade. Do começo restrito às operações fabris como pintura e soldagem, até o início de seu uso nas residências, apenas algumas décadas se passaram. A robótica social é uma área de pesquisa que visa desenvolver modelos para que a interação direta de robôs com seres humanos ocorra de forma natural. Um dos fatores que compromete a rápida evolução da robótica social é a dificuldade em integrar sistemas cognitivos e robóticos, principalmente devido ao volume e complexidade da informação produzida por um mundo caótico repleto de dados sensoriais. Além disso, a existência de múltiplas configurações de robôs, com arquiteturas e interfaces distintas, dificulta a verificação e repetibilidade dos experimentos realizados pelos diversos grupos de pesquisa. Esta tese contribui para a evolução da robótica social ao definir uma arquitetura, denominada Cognitive Model Development Environment (CMDE) que simplifica a conexão entre sistemas cognitivos e robóticos. Essa conexão é formalizada com uma ontologia, denominada OntPercept, que modela a percepção do ambiente a partir de informações sensoriais captadas pelos sensores presentes no agente robótico. Nos últimos anos, diversas ontologias foram propostas para aplicações robóticas, mas elas não são genéricas o suficiente para atender completamente às necessidades das áreas de robótica e automação. A formalização oferecida pela OntPercept facilita o desenvolvimento, a reprodução e a comparação de experimentos associados a robótica social. A validação do sistema proposto ocorre com suporte do simulador Robot House Simulator (RHS), que fornece um ambiente onde, o agente robótico e o personagem humano podem interagir socialmente com níveis crescentes de processamento cognitivo. A proposta da CMDE viabiliza a utilização de qualquer sistema cognitivo, em particular, o experimento elaborado para validação desta pesquisa utiliza Soar como arquitetura cognitiva. Em conjunto, os elementos: arquitetura CMDE, ontologia OntPercept e simulador RHS, todos disponibilizados livremente no GitHub, estabelecem um ambiente completo que propiciam o desenvolvimento de experimentos envolvendo sistemas cognitivos dirigidos para a área de robótica social. / The use of robots in modern society is a reality. From the beginning restricted to the manufacturing operations like painting and welding, until the beginning of its use in the residences, only a few decades have passed. Social robotics is an area that aims to develop models so that the direct interaction of robots with humans occurs naturally. One of the factors that compromises the rapid evolution of social robotics is the difficulty in integrating cognitive and robotic systems, mainly due to the volume and complexity of the information produced by a chaotic world full of sensory data. In addition, the existence of multiple configurations of robots, with different architectures and interfaces, makes it difficult to verify and repeat the experiments performed by the different research groups. This research contributes to the evolution of social robotics by defining an architecture, called Cognitive Model Development Environment (CMDE), which simplifies the connection between cognitive and robotic systems. This connection is formalized with an ontology, called OntPercept, which models the perception of the environment from the sensory information captured by the sensors present in the robotic agent. In recent years, several ontologies have been proposed for robotic applications, but they are not generic enough to fully address the needs of robotics and automation. The formalization offered by OntPercept facilitates the development, reproduction and comparison of experiments associated with social robotics. The validation of the proposed system occurs with support of the Robot House Simulator (RHS), which provides an environment where the robotic agent and the human character can interact socially with increasing levels of cognitive processing. All together, the elements: CMDE architecture, OntPercept ontology and RHS simulator, all freely available in GitHub, establish a complete environment that allows the dev

This research aims to find distinct and crucial factors needed in order to design a better robot through exploring the meaning of movement. The researcher conducted six-weeks of iterative work to collect data via an ethnographic method. The researcher examined the interactions between a telepresence robot and human beings in an authentic environment through the collected data and analyzed it based on proxemics theory. The research observed that the robot was given social space when it approached the participants with pauses in between movements. Furthermore, the research introduces proxemics pivot and its notion. Proxemics pivot refers to the part of the robot that people perceive as a standard point when they adjust the proximity between the robot and themselves. The proxemics pivot was considered “a face” and was attributed social properties; the other parts of the robot did not receive the same consideration.

In zahlreichen Forschungsprojekten wird unter Einsatz großer finanzieller und personeller Ressourcen daran gearbeitet, dass Roboter die Fabrikhallen verlassen und Teil von Alltagswelten wie Krankenhäusern, Kindergärten und Privatwohnungen werden. Die Konstrukteurinnen und Konstrukteure stehen dabei vor einer nicht-trivialen Herausforderung: Sie müssen die Ambivalenzen und Kontingenzen alltäglicher Interaktion in die diskrete Sprache der Maschinen übersetzen. Wie sie dieser Herausforderung begegnen, welche Muster und Lösungen sie heranziehen und welche Implikationen für die Verwendung von Sozialrobotern dabei gelegt werden, ist der Gegenstand des Buches. Auf der Suche nach der Antwort, was Roboter sozial macht, hat Andreas Bischof Forschungslabore und Konferenzen in Europa und Nordamerika besucht und ethnografisch erforscht. Zu den wesentlichen Ergebnissen dieser Studie gehört die Typologisierung von Forschungszielen in der Sozialrobotik, eine epistemische Genealogie der Idee des Roboters in Alltagswelten, die Rekonstruktion der Bezüge zu 'echten' Alltagswelten in der Sozialrobotik-Entwicklung und die Analyse dreier Gattungen epistemischer Praktiken, derer sich die Ingenieurinnen und Ingenieure bedienen, um Roboter sozial zu machen.