AURORAis a software platform, that facilitates scalable deployment of robotic simulations over the web for the Human-Robot Interaction (HRI) community. As robotics is becoming increasingly important in various disciplines, there is a growing need for accessible and scalable research methods. Traditional experiments often require expensive hardware and in-person participation, limiting accessibility and participant diversity. Our platform allows researchers from different fields to easily provide HRI experiences by deploying online studies with robotic simulations paired with customizable surveys, allowing end users worldwide to interact with these simulations. Our platform is entirely open source and can be hosted locally, providing flexibility and control of the research environment. Since AURORA is implemented with Docker, it is platform-independent. By offering a user-friendly interface that can be deployed and used without extensive technical expertise, our platform reduces costs, increases participant diversity, and improves the reproducibility of research in the HRI community.

Traditional digital tools for exploring historical data mostly rely on conventional 2D visualizations, which often cannot reveal all relevant interrelationships between historical fragments (e.g., persons or events). In this paper, we present a novel interactive exploration tool for historical data in VR, which represents fragments as spheres in a 3D environment and arranges them around the user based on their temporal, geo, categorical and semantic similarity. Quantitative and qualitative results from a user study with 29 participants revealed that most participants considered the virtual space and the abstract fragment representation well-suited to explore historical data and to discover complex interrelationships. These results were particularly underlined by high usability scores in terms of attractiveness, stimulation, and novelty, while researching historical facts with our system did not impose unexpectedly high task loads. Additionally, the insights from our post-study interviews provided valuable suggestions for future developments to further expand the possibilities of our system.

It is commonly assumed that explanations should be tailored to the addressee in order to yield higher understanding. Consequently, much work on explainable intelligent agents has been directed to user-adapted explanations. However, recent studies show ambiguous results with regard to the efficiency of adaptive and non-adaptive explanations. This raises the question whether an explanation, generated by a socially interactive agent, should be adapted. In this paper, we present a general approach to adaptive explanation generation as a non-stationary decision process, and we study the benefits and pitfalls of adapting explanations in an ongoing interaction with a user. Specifically, we report results from a between-subject online evaluation in a game explanation domain with three conditions (non-interactive, interactive but non-adaptive, adaptive). Results show that the decision for or against adaptivity depends on the goal of the explanation, the complexity of the domain and external constraints. Based on the collected data we discuss challenges that arise from the individuality of adaptive dialogues, such as comparability and the tendency to produce results with a large variance.