Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. We validated the results of the Market Patrol scenario by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. 3. We modeled the movement of a socially aware mobile robot in a dense crowds, using the concept of a micro-conflict to represent the challenge of giving or not giving way to pedestrians. 4. We developed an approach for the robot behavior in micro-conflicts based on the psychological observation that human opponents will use a consistent strategy. For this, the mobile robot classifies the opponent strategy reflected by the personality and social status of the person and chooses an appropriate counter-strategy that takes into account the urgency of the robots' mission. 5. We developed an alternative approach for the resolution of micro-conflicts based on the imitation of the behavior of the human agent. This approach aims to make the behavior of an autonomous robot closely resemble that of a remotely operated one.

Maintaining cognitive health is often a pressing concern for aging adults, and given the world's shifting age demographics, it is impractical to assume that older adults will be able to rely on individualized human support for doing so. Recently, interest has turned toward technology as an alternative. Companion robots offer an attractive vehicle for facilitating cognitive exercise, but the language technologies guiding their interactions are still nascent; in elder-focused human-robot systems proposed to date, interactions have been limited to motion or buttons and canned speech. The incapacity of these systems to autonomously participate in conversational discourse limits their ability to engage users at a cognitively meaningful level. I addressed this limitation by developing a platform for human-robot book discussions, designed to promote cognitive exercise by encouraging users to consider the authors' underlying intentions in employing creative metaphors. The choice of book discussions as the backdrop for these conversations has an empirical basis in neuro- and social science research that has found that reading often, even in late adulthood, has been correlated with a decreased likelihood to exhibit symptoms of cognitive decline. The more targeted focus on novel metaphors within those conversations stems from prior work showing that processing novel metaphors is a cognitively challenging task, for young adults and even more so in older adults with and without dementia. A central contribution arising from the work was the creation of the first computational method for modelling metaphor novelty in word pairs. I show that the method outperforms baseline strategies as well as a standard metaphor detection approach, and additionally discover that incorporating a sentence-based classifier as a preliminary filtering step when applying the model to new books results in a better final set of scored word pairs. I trained and evaluated my methods using new, large corpora from two sources, and release those corpora to the research community. In developing the corpora, an additional contribution was the discovery that training a supervised regression model to automatically aggregate the crowdsourced annotations outperformed existing label aggregation strategies. Finally, I show that automatically-generated questions adhering to the Questioning the Author strategy are comparable to human-generated questions in terms of naturalness, sensibility, and question depth; the automatically-generated questions score slightly higher than human-generated questions in terms of clarity. I close by presenting findings from a usability evaluation in which users engaged in thirty-minute book discussions with a robot using the platform, showing that users find the platform to be likeable and engaging.

Implicit communication is important in interaction because it plays a role in conveying the internal mental states of an individual. For example, emotional expressions that are shown through unintended facial gestures can communicate underlying affective states. People can infer mental states from implicit cues and have strong expectations of what those cues mean. This is true for human-human interactions, as well as human-robot interactions. A Normalizing flow model is used as a generative model that can produce facial gestures and head movements. The invertible nature of the Normalizing flow model makes it possible to manipulate attributes of the generated gestures. The model in this work is capable of generating facial expressions that look real and human-like. Furthermore, the model can manipulate the generated output to change the perceived affective state of the facial expressions. / Implicit kommunikation är viktig i interaktioner eftersom den spelar en roll för att förmedla individens inre mentala tillstånd. Till exempel kan känslomässiga uttryck som visas genom oavsiktliga ansiktsgester kommunicera underliggande affektiva tillstånd. Människor kan härleda mentala tillstånd från implicita ledtrådar och har starka förväntningar på vad dessa ledtrådar betyder. Detta gäller för interaktion mellan människor, liksom interaktion mellan människa och robot. En normaliserande flödesmodell används som en generativ modell som kan producera ansiktsgester och huvudrörelser. Den inverterbara naturen hos normaliseringsflödesmodellen gör det också möjligt att manipulera det genererade ansiktsuttrycken. Utgången manipuleras i två dimensioner som vanligtvis används för att beskriva affektivt tillstånd, valens och upphetsning. Modellen i detta arbete kan generera ansiktsuttryck som ser verkliga och mänskliga ut och kan manipuleras for att ändra det affektiva tillstånd.

Affective computing is an essential part of Human-Robot Interaction, where knowing the human’s emotional state is crucial to create an interactive and adaptive social robot. Previous work has mainly been focusing on using unimodal or multimodal sequential models for Affective State Recognition. However, few have included context-based information with their models to boost performance. In this paper, context-based features are tested on a multimodal Gated Recurrent Unit model with late fusion on game oriented data. It shows that using context-based features such as game state can significantly increase the performance of sequential multimodal models on game oriented data. / Affektiv beräkning är en viktig del av interaktion mellan människa och robot, där kunskap om människans emotionella tillstånd är avgörande för att skapa en interaktiv och anpassningsbar social robot. Tidigare arbete har främst fokuserat på att använda unimodala eller multimodala sekventiella modeller för affektiv tillståndsigenkänning. Men få har inkluderat kontextbaserad information i sin inställning för att öka prestanda. I denna uppsats testas kontextbaserade funktioner på en multimodal s.k. Gated Recurrent Unit modell med sen fusion på spelorienterad data. Det visar att användning av kontextbaserade information som tillståndet i spelet kan avsevärt öka prestandan hos sekventiella multimodala modeller på spelorienterad data.

Natural Language Generation and generating believable verbal communication are critical components in the development of social robots. The work presented in this paper is based on the sequence-to-sequence model and is focused on improving context and repeatability through the inclusion of task- specific information. The data set on which this study was conducted was collected through a Wizard of Oz framework using a social robot. The generated dialogue was evaluated through a survey designed to measure the adherence to the game context and perceived human qualities. The human qualities were measured using attributes from two well-known attribute scales intended for evaluating Human-Robot Interaction. The evaluation results indicate that the quality of the generated dialogue is on par with examples of actual dialogue spoken during the experiments. This paper also highlights interesting aspects regarding the usefulness of transfer learning in narrow contextual applications. The results presented in this paper show that it is possible to improve the contextual nature of generated dialogue by including additional task-specific information. / Generering av naturligt språk och uppgiften att skapa trovärdig verbal kommunikation är kritiska komponenter i utvecklingen av sociala robotar. Arbetet som presenteras i denna uppsats är baserat på sekvens-till-sekvens-modellen och fokuserar på att förbättra sammanhang och repeterbarhet genom att inkludera uppgiftspecifik information. Datauppsättningen som denna studie genomförde samlades in via ett Wizard of Oz-ramverk med hjälp av en social robot. Den genererade dialogen utvärderades genom en onlineundersökning utformad för att mäta efterlevnaden av spelskontexten och upplevda mänskliga egenskaper. Dessa mänskliga egenskaper mättes med attribut från två välkända attributskalor avsedda för utvärdering av människa-robot-interaktion. Utvärderingsresultaten visar att kvaliteten på den genererade dialogen är i nivå med exempel på faktisk dialog som talats under experimenten. Denna uppsats belyser också intressanta aspekter beträffande nyttan av överföringsinlärning i smala kontextuella applikationer. Resultaten som presenteras i denna uppsats visar att det är möjligt att förbättra den kontextuella karaktären hos genererad dialog genom att inkludera ytterligare uppgiftspecifik information.

With the ongoing progress of research in robotics, computer vision and artificial intelligence, robots are becoming more complex, their functionalities are increasing and their abilities to solve particular problems get more efficient. For these robots to share with us our lives and environment, they should be able to move autonomously and be operated easily by users. The main focus of this thesis is on the differences and similarities in a human to a human versus a human to a robot interaction in an office environment. Experimental methods are used to identify these differences and similarities and arrive at an understanding about how users perceive robots and the robots’ abilities to help in the development of interactive service robots that are able to navigate and perform various tasks in a real life environment. A user study was conducted where 14 subjects were observed while presenting an office environment to a mobile robot and then to a person. The results from this study were that users used the same verbal phrases, hand gestures, gaze, etc. to present the environment to the robot versus a person but they emphasized more by identifying the different items to the robot .The subjects took less time to show a person around than the robot. / Genom forskning i robotik, datorseende och artificiell intelligens kommer robotar att bli mer och mer komplexa. Robotars funktionalitet ökar ständigt och deras kapacitet att lösa specifika problem blir mer effektiv. För att dessa robotar ska finnas i våra vardagsliv och vår miljö måste de kunna röra sig självständigt (autonomt) och de måste vara lätta att hantera för användare. Detta examensarbete fokuserar på skillnader och likheter mellan interaktion människa - människa och robot - människa i en kontorsmiljö. Med hjälp av experimentell metod är det möjligt att upptäcka dessa skillnader och likheter och därmed förstå hur människor uppfattar robotar och deras förmåga. Detta kan bidra till utvecklingen av servicerobotar som kan navigera och utföra olika uppgifter i vardagslivet. En användarstudie utfördes med 14 försökspersoner som observerades medan de presenterade en kontorsmiljö både för en människa och för en robot. Resultatet av denna studie var att försökspersonerna använde samma typer av muntliga uttryck och handgester, blickar, osv. för att presentera miljön för en människa som för roboten. De uttryckte sig mer detaljerat för roboten när det gällde att identifiera olika föremål i miljön. Försökspersonerna behövde mer tid för att presentera miljön för roboten än en människa.

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Portrayals of artificially intelligent robots are becoming increasingly prevalent in children’s culture. This affects how children perceive robots, which have been found to affect the way children in school understand subjects like technology and programming. Since teachers need to know what influences their pupils' understanding of these subjects, we need to know how children’s preconceptions about robots affect the way they attribute mental states to them. We still know relatively little about how children do this. Based on the above, a qualitative approach was deemed fit. This study aimed to (1) investigate the reasoning and preconceptions underlying children’s mental state attribution to robots, and (2) explore the effectiveness of moral questions as a way to do this. 16 children aged 5- and 6 years old were asked to rate the mental states of four different robots while subsequently being asked to explain their answers. Half of the children were interviewed alone and half in small groups. A thematic analysis was conducted to analyze the qualitative data. Children’s mental state attribution was found to be influenced by preconceptions about robots as a group of entities lacking mental states. Children were found to perceive two robots, Atlas, and Nao, differently in various respects. This was argued to be because the children perceived these robots through archetypal frameworks. Moral questions were found successful as a way to spark reflective reasoning about the mental state attribution in the children.

Robot programming can be an expensive and tedious task and companies may have to employ dedicated staff. A promising framework that can alleviate some of the most repetitive tasks and potentially make robots more accessible to non-experts is Learning from Demonstration (LfD). LfD is a framework where the robot learns how to solve a task by observing a human demonstrating it. A representation of the learned policy is needed and Behavior Trees (BTs) are promising. They are a representation of a controller that organizes the switching between tasks and naturally provides the modularity required for learning and the reactivity required for operating in an uncertain environment. Furthermore, BTs are transparent, allowing the user to inspect the policy and verify its safety before executing it. Learning BTs from demonstration has not been studied much in the past. The aim of this thesis is therefore to investigate the feasibility of using BTs in the context of LfD and how such a structure could be learned. To evaluate the feasibility of BTs and answering how they can be learned, a new algorithm for learning BTs from demonstration is presented and evaluated. The algorithm detects similarities between multiple demonstrations to infer in what reference frames different parts of a task occur. The similarities are also used to detect hidden task constraints and goal conditions that are given to a planner that outputs a reactive task plan in the form of a BT. The algorithm is evaluated on manipulation tasks in both simulation and a real robot. The results show that the resulting BT can successfully solve the task while being robust to initial conditions and reactive towards disturbances. These results suggest that BTs are a suitable policy representation for LfD. Furthermore, the results suggest that the presented algorithm is capable of learning a reactive and fault-tolerant task plan and can be used as a basis for future algorithms. / Robotprogrammering kan vara kostsamt och repetitivt och företag kan behöva anställa särskild personal. Ett lovande ramverk som kan underlätta några av de mest repetitiva uppgifterna och potentiellt göra robotar mer tillgängliga för icke-experter är Inlärning från Demonstrationer (eng. Learning from Demonstration, LfD). LfD är ett ramverk där roboten lär sig lösa en uppgift genom att observera hur en människa gör det. En representation av den inlärda policyn behövs och Beteendeträd (eng. Behavior Trees, BTs) är lovande. De är en representation av en kontroller som organiserar växlandet mellan olika uppgifter och tillhandahåller naturligt den moduläritet som krävs för lärande och den reaktivitet som krävs för att verka i en oviss miljö. Dessutom är BTs transparenta, vilket gör det möjligt för användaren att inspektera policyn och verifiera dess säkerhet innan den körs. Att lära sig BTs från demonstrationer har inte studerats mycket tidigare. Syftet med det här arbetet är därför att undersöka genomförbarheten av att använda BTs inom sammanhanget av LfD och hur en sådan struktur kan läras. För att utvärdera genomförbarheten hos BTs och svara på hur de kan läras in presenteras och utvärderas en ny algoritm för inlärning av BTs. Algoritmen detekterar likheter mellan flera demonstrationer för att avgöra i vilken referensram olika delar av uppgiften sker. Likheterna används även för att upptäcka dolda bivillkor och målvillkor i uppgiften som ges till en planerare som skapar en reaktiv uppgiftsplan i form av en BT. Algoritmen utvärderas på manipuleringsuppgifter både i simulering och på en verklig robot. Resultaten visar att de resulterande BTs kan lösa uppgifterna med framgång och samtidigt vara robusta mot begynnelsevillkor och reaktiva mot störningar. Resultaten antyder att BTs är lämpade som en policyrepresentation för LfD. Vidare antyder resultaten att den presenterade algoritmen är kapabel att lära sig en reaktiv och feltolerant uppgiftsplan och kan användas som en utgångspunkt för framtida algoritmer.

Postpartum Depression (PPD) affects 8-15 percent of new mothers in Sweden every year. The majority of PPD cases go undetected, and only a few percent receive adequate care. New ways to detect and diagnose PPD are required. In my previous work, PPD experts expressed willingness to integrate social assistive robots (SARs) into their medical team. Moreover, we disclosed that future work was needed to include patients' perspectives on the subject. This thesis aims to provide insights from mothers with experience of mental health issues in relation to their pregnancy to elicit perceptions, attitudes and opinions towards SARs in PPD healthcare. Semi-structured interviews with participants (n=10) and a generative design activity were conducted and analyzed using thematic analysis. The results suggest split opinions towards SARs in PPD healthcare. Participants expressed healthcare needs, and how SARs could be used to address these issues. Opinions towards the robot's appearance, including characteristics, gender and ethnicity were also discussed. Future work including midwives, child health nurses' and perspectives is needed, as well as a larger sample of women to validate the robot’s appearance, gender and characteristics.