--This work presents REFLEX: Robotic Explanations to FaiLures and Human EXpressions, a comprehensive mul-timodal dataset capturing human reactions to robot failures and subsequent explanations in collaborative settings. It aims to facilitate research into human-robot interaction dynamics, addressing the need to study reactions to both initial failures and explanations, as well as the evolution of these reactions in long-term interactions. By providing rich, annotated data on human responses to different types of failures, explanation levels, and explanation varying strategies, the dataset contributes to the development of more robust, adaptive, and satisfying robotic systems capable of maintaining positive relationships with human collaborators, even during challenges like repeated failures. I NTRODUCTION As robots become increasingly integrated into our everyday lives, from homes and workplaces to public spaces, the need to understand and improve human-robot interaction (HRI) has never been more critical. Despite significant advancements in robotics, they are still prone to failures, ranging from minor glitches to serious malfunctions.

To facilitate human-robot interaction and gain human trust, a robot should recognize and adapt to changes in human behavior. This work documents different human behaviors observed while taking objects from an interactive robot in an experimental study, categorized across two dimensions: pull force applied and handedness. We also present the changes observed in human behavior upon repeated interaction with the robot to take various objects.

Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot's explanation level at a certain round but also the prior information they received from the robot.

Although initially emerging in psychology, many of the concerns raised, such as lack of open access to data, materials, and/or experimental design apply also to other (social) sciences. Among these are both Human Robot Interaction (HRI) and its related sub-field of Child Robot Interaction (CRI), where social and psychological relationships between humans and robots are often the focus of the research. Given its novelty and rapidly evolving progress, CRI in particular suffers from fragmented and heterogeneous literature, varying research goals, and a lack of standardised methods and metrics. Recent efforts have brought forth conversations related to replication specifically within CRI [51, 52], with authors appealing for more works that address the main challenges in HRI with children whilst still ensuring high-quality reporting and data sharing. However, clear open science guidelines on reproducibility in HRI and related sub-fields are still missing.

Children growing up in the era of Artificial Intelligence (AI) will be most impacted by the technology across their life span. Participatory Design (PD) is widely adopted by the Interaction Design and Children (IDC) community, which empowers children to bring their interests, needs, and creativity to the design process of future technologies. While PD has drawn increasing attention to human-centered AI design, it remains largely untapped in facilitating the design process of AI technologies relevant to children and their community. In this paper, we report intriguing children's design ideas on AI technologies resulting from the "Research and Design Challenge" of the 22nd ACM Interaction Design and Children (IDC 2023) conference. The diversity of design problems, AI applications and capabilities revealed by the children's design ideas shed light on the potential of engaging children in PD activities for future AI technologies. We discuss opportunities and challenges for accessible and inclusive PD experiences with children in shaping the future of AI-powered society.

Despite great advances in what robots can do, they still experience failures in human-robot collaborative tasks due to high randomness in unstructured human environments. Moreover, a human's unfamiliarity with a robot and its abilities can cause such failures to repeat. This makes the ability to failure explanation very important for a robot. In this work, we describe a user study that incorporated different robotic failures in a human-robot collaboration (HRC) task aimed at filling a shelf. We included different types of failures and repeated occurrences of such failures in a prolonged interaction between humans and robots. The failure resolution involved human intervention in form of human-robot bidirectional handovers. Through such studies, we aim to test different explanation types and explanation progression in the interaction and record humans.