Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) empower users to experience digital realities. Known as distinct technology classes, the lines between them are becoming increasingly blurry with recent technological advancements. New systems enable users to interact across technology classes or transition between them—referred to as cross-reality systems . Nevertheless, these systems are not well understood. Hence, in this article, we conducted a scoping literature review to classify and analyze cross-reality systems proposed in previous work. First, we define these systems by distinguishing three different types. Thereafter, we compile a literature corpus of 306 relevant publications, analyze the proposed systems, and present a comprehensive classification, including research topics, involved environments, and transition types. Based on the gathered literature, we extract nine guiding principles that can inform the development of cross-reality systems. We conclude with research challenges and opportunities.

Robot training often takes place in simulated environments, particularly with reinforcement learning. Therefore, multiple training environments are generated using domain randomization to ensure transferability to real-world applications and compensate for unknown real-world states. We propose improving domain randomization by involving human application experts in various stages of the training process. Experts can provide valuable judgments on simulation realism, identify missing properties, and verify robot execution. Our human-in-the-loop workflow describes how they can enhance the process in five stages: validating and improving real-world scans, correcting virtual representations, specifying application-specific object properties, verifying and influencing simulation environment generation, and verifying robot training. We outline examples and highlight research opportunities. Furthermore, we present a case study in which we implemented different prototypes, demonstrating the potential of human experts in the given stages. Our early insights indicate that human input can benefit robot training at different stages.

Nowadays, deep learning models enable numerous safety-critical applications, such as biometric authentication, medical diagnosis support, and self-driving cars. However, previous studies have frequently demonstrated that these models are attackable through slight modifications of their inputs, so-called adversarial attacks. Hence, researchers proposed investigating examples of these attacks with explainable artificial intelligence to understand them better. In this line, we developed an expert tool to explore adversarial attacks and defenses against them. To demonstrate the capabilities of our visualization tool, we worked with the publicly available CIFAR-10 dataset and generated one-pixel attacks. After that, we conducted an online evaluation with 16 experts. We found that our tool is usable and practical, providing evidence that it can support understanding, explaining, and preventing adversarial examples.