Adaptive human-robot interaction in sensorimotor task instruction: From human to robot dance tutors

Den generella tilliten har i Sverige länge varit stabil och anses ur ett internationellt perspektiv vara unik. Syftet med denna studie är att studera om det finns ett samband mellan generell tillit och xenofobi. På grund av den minskade toleransen till främlingar under de senaste åren, studeras även hur generell den svenska tilliten egentligen är, eller om den generella tilliten är beroende av gruppspecifik tillit. Studien grundar sig på Robert Putnams teori om socialt kapital samt tidigare forskning som visat på sambandet mellan generell tillit och xenofobi. Studien använder sig av data från World Value Survey och behandlar svensk data från 2011. Studiens frågeställningar har besvarats genom en logistisk regressionsanalys. Resultaten visade ett positivt samband mellan låg in-gruppstillit och xenofobi samt ett positivt samband mellan låg ut-gruppstillit och xenofobi. Slutsatsen blev att det fanns ett samband mellan generell tillit och xenofobi. Detta samband kunde även till viss del förklaras av gruppspecifik tillit, där särskilt tillit till individer från andra kulturella sfärer än den egna var av betydelse

An emotion and memory model for social robots : a long-term interaction

In this thesis, we investigate the role of emotions and memory in social robotic companions. In particular, our aim is to study the effect of an emotion and memory model towards sustaining engagement and promoting learning in a long-term interaction. Our Emotion and Memory model was based on how humans create memory under various emotional events/states. The model enabled the robot to create a memory account of user's emotional events during a long-term child-robot interaction. The robot later adapted its behaviour through employing the developed memory in the following interactions with the users. The model also had an autonomous decision-making mechanism based on reinforcement learning to select behaviour according to the user preference measured through user's engagement and learning during the task. The model was implemented on the NAO robot in two different educational setups. Firstly, to promote user's vocabulary learning and secondly, to inform how to calculate area and perimeter of regular and irregular shapes. We also conducted multiple long-term evaluations of our model with children at the primary schools to verify its impact on their social engagement and learning. Our results showed that the behaviour generated based on our model was able to sustain social engagement. Additionally, it also helped children to improve their learning. Overall, the results highlighted the benefits of incorporating memory during child-Robot Interaction for extended periods of time. It promoted personalisation and reflected towards creating a child-robot social relationship in a long-term interaction

USING DEEP LEARNING-BASED FRAMEWORK FOR CHILD SPEECH EMOTION RECOGNITION

Biological languages of the body through which human emotion can be detected abound including heart rate, facial expressions, movement of the eyelids and dilation of the eyes, body postures, skin conductance, and even the speech we make. Speech emotion recognition research started some three decades ago, and the popular Interspeech Emotion Challenge has helped to propagate this research area. However, most speech recognition research is focused on adults and there is very little research on child speech. This dissertation is a description of the development and evaluation of a child speech emotion recognition framework. The higher-level components of the framework are designed to sort and separate speech based on the speaker’s age, ensuring that focus is only on speeches made by children. The framework uses Baddeley’s Theory of Working Memory to model a Working Memory Recurrent Network that can process and recognize emotions from speech. Baddeley’s Theory of Working Memory offers one of the best explanations on how the human brain holds and manipulates temporary information which is very crucial in the development of neural networks that learns effectively. Experiments were designed and performed to provide answers to the research questions, evaluate the proposed framework, and benchmark the performance of the framework with other methods. Satisfactory results were obtained from the experiments and in many cases, our framework was able to outperform other popular approaches. This study has implications for various applications of child speech emotion recognition such as child abuse detection and child learning robots

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers