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Robots are anticipated to communicate with humans in our social context. Using nonverbal communication by robots increases communication comprehension because humans intuitively understand such behavior due to their experience of human-human interaction. Thus, this behavior is suitable for studying what makes robot motion intuitive to understand in human-robot interaction (HRI). We study how robot features help humans intuitively grasp expressive robot gestures, concentrating on what makes behavior easy to interpret. After watching eighteen nine-second videos of three robot kinds demonstrating expressive robot actions, 50 participants completed an open-ended survey. Our findings highlight the inputs, mediating factors, and outputs that users reported based on observing examples of expressive robot behavior. These insights are a starting point for analyzing robot behavior from the perspective of intuition and provide a foundation for a theoretical framework for intuition in HRI.

This study investigates the impact of dynamic matching of robot motion velocity to users' walking velocity in a human-robot approach scenario on three categories: perceived comfort, interactivity, and naturalness. Considering age diversity, participants were divided into two age groups. Young participants tended to rate higher for all three categories when the robot's approaching velocity was dynamically matched. In contrast, elderly participants preferred a steady and slow robot approach for comfort and predictability. These findings contribute to the ongoing effort to design assistive robots that effectively cater to diverse user groups, ultimately enhancing user satisfaction and acceptance. Taken together, this study highlights the importance of dynamically tailoring robot behaviors based on user demographics for positive Human-Robot Interactions.

Privacy has recently got attention, especially since the introduction of the General Data Protection Regulation (GDPR) in Europe, the new Artificial Intelligence Act (AIA) and the new Machinery Regulation. Privacy can be defined as someone’s right to keep personal matters, including personal life, personal information, or relationships, to themselves. A social robot’s appearance (=the combination of embodiment and motion) may contribute to how human users perceive them, including how these robots are perceived in relation to privacy. If these robots are part of certain services such as home- or healthcare, these may also have consequences on how these services are perceived. This study aims at showcasing the users’ perception of privacy based on the perceived robot’s appearance. Three social robots were chosen for this purpose: PLEO (with a zoomorphic appearance), Pepper (with a childlike anthropomorphic appearance), and TIAGo (with a mechanical and asymmetrical appearance). The data was collected through an in-lab observational video-based study from 50 participants with very limited- or no experience with robots. Our findings show that PLEO was perceived as preserving most of the users' privacy, while Pepper was perceived as more privacy-invasive than PLEO but less than TIAGo. TIAGo was perceived as hard to interpret in terms of privacy. Our findings also point out that designing robots with a cute appearance, such as PLEO, may contribute to participants trusting the robot more and thus being willing to share their data. The paper provides a list of characteristics that participants associated with a social robot as preserving or not their privacy. Further, the paper discusses the appearance of these social robots in terms of “cuteness” as a dark pattern in the design of social robots that may lead to data myopia, but also the possible consequences this may have, for vulnerable users, while trying to design more inclusive robots.

Robots are becoming part of our social landscape. Social interaction with humans must be efficient and intuitive to understand because nonverbal cues make social interactions between humans and robots more efficient. This study measures mental effort to investigate factors influencing the intuitive understanding of expressive nonverbal robot motions. Using an eye tracker to measure pupil response and gaze, fifty participants were asked to watch eighteen short video clips featuring three different types of robots performing expressive robot behaviors. Our findings indicate that the appearance of the robot, the viewing angle, and the expression shown by the robot all influence the cognitive load. Therefore, they may affect the intuitive understanding of expressive robot behavior. Furthermore, we found differences in the fixation time for different features of the various robots. With these insights, we identified possible improvement directions for making interactions between humans and robots more efficient and intuitive.

As the world of competitive sports increasingly embraces data-driven techniques, our research explores the potential of machine learning in distinguishing elite from semi-elite beach volleyball players. This study is motivated by the need to understand the subtle yet crucial differences in player movements that contribute to high-level performance in beach volleyball. Utilizing advanced machine learning techniques, we analyzed specific movement patterns of the motion of the torso during spikes, captured through vest-mounted accelerometers. Our approach offers novel insights into the nuanced dynamics of elite play, revealing that certain movement patterns are distinctly characteristic of higher skill levels. One of our key contributions is the ability to classify spiking movements at different skill levels with an accuracy rate as high as 87%. This current research provides a foundation of what separates elite players from their semi-elite counterparts.

Artificial Intelligence requires trust to be fully utilised by users and for them to feel safe while using them. Trust, and indirectly, a sense of safety, has been overlooked in the pursuit of more accurate or better-performing black box models. The field of Explainable Artificial Intelligence and the current recommendations and regulations around Artificial Intelligence require more transparency and accountability from governmental and private institutes. Creating a self-explainable AI that can be used to solve a problem while explaining its reasoning is challenging to develop. Still, it would be unable to explain all the other AIs without self-explainable abilities. It would likely not function for different problem domains and tasks without extensive knowledge about the model. The solution proposed in this thesis is the Accountability Module. It is meant to function as an external explanatory module, which would be able to function with different AI models in different problem domains. The prototype was inspired by accident investigations regarding autonomous vehicles but was created and implemented for a simplified simulation of vehicles driving on a highway. The prototype's goal was to attempt to assist an investigator in understanding why the vehicle crashed. The Accountability Module found the main factors in the decision that resulted in an accident. It was also able to facilitate the answering of whether the outcome was avoidable and if there were inconsistencies with the agent's logic by examining different cases against each other. The prototype managed to provide useful explanations and assist investigators in understanding and troubleshooting agents. The thesis and the Accountability Module indicate that a similar explanatory module is a robust direction to explore further. The chosen explainability methods and techniques were highly connected to the problem domain and limited by the scope of the thesis. Therefore, a more extensive test of the prototype with different problems needs to be performed to check the system's rigidity and versatility as well as the significance of the results. Nevertheless, in a collaboration between an Accountability Module expert and a domain expert, I expect a modular explainability solution to create more insight into an AI model and its significant incidents.