The field of robotics has shown significant advances in autonomous systems, particularly in robot navigation. Since the decisions made during navigation can be difficult for human operators to understand, research aims to provide explanations that improve human-robot interaction (HRI). However, generating and designing such explanations with the intention of improving robot-related human factors is still an ongoing research challenge. This paper addresses this challenge by investigating the impact of different explanation types on a set of human factors in the context of robot navigation. For this purpose, we conducted a user study that examined the impact of six different explanation types on commonly used human factors, including trust, satisfaction, situation awareness, likeability, understandability, and perceived usefulness. Additionally, the study provides indications of their general applicability for robot navigation explanations through creation of sum ranks across the observed human factor metrics. The results show that depending on the chosen explanation type, a significant impact on the measured factors can be observed. While constraint-based explanations are generally rated highly across all factors, apologetic explanations are not perceived well across all measured human factors. Our results provide insights into the impact of explanation types used for robot navigation scenarios on robot-related human factors, and also provide practical insights for designing explanations for robot navigation scenarios.

There is an interest in and need for the use of semiautonomous robots in various fields, such as disaster response. Over the years, different techniques for semiautonomous control were developed, ranging from teleoperation guidance to different user interface designs for setting task constraints and goals interactively. Among those, affordance templates emerged as a recent method for users to efficiently provide robots with contextual information about object shapes, properties, and affordances. In many fields where direct teleoperation is common, affordance templates seem to be a promising candidate for improving performance and usability. However, despite the reports on the potential benefits of this technique in comparison to direct teleoperation, they are often qualitative or focus on tasks where teleoperation is particularly challenging. This can be a problem because task difficulty can influence different performance metrics and human factors, so results from studies that show large differences in task difficulty between interaction modes cannot be directly generalized to tasks with smaller or non-existent differences in difficulty. In this study, we aim to evaluate the effectiveness of affordance templates in the context of debris removal for disaster response, a task where direct teleoperation is a viable technique. We compared the two methods in a simulated setting through a user study involving 41 participants by measuring a) usability through questionnaires and b) performance on secondary tasks, an established measure of spare information processing capacity. The study results show that despite similar difficulty, users performed better on secondary tasks when using affordance template-based semiautonomy in this setting.

Hybrid Collaboration, where remote and co-located team members work together using different devices and tools, has already been trending in recent years (e.g., through globalization and international cooperation) but experienced a further boost since the outbreak of the COVID-19 pandemic. The reason behind this surge in hybrid practices is probably that the crisis revealed aspects of remote collaboration which proved functional and which many decision makers (in industry as well as academia) plan to retain for the future. Thus, hybrid collaboration is an extremely timely topic which should be further studied in the context of CSCW. One major CSCW-anchored concept that has most intensively been researched in co-located collaboration settings where it is usually inherently related to spatial aspects and proximity, is territoriality. Already work on territoriality in fully distributed, remote settings has shown that there are significant differences due to the characteristics of the scenario. In this paper, we focus on territoriality in hybrid settings where we identified a significant research gap, and present the results of a user study with 22 teams consisting of four people each (distributed across two locations at two different universities), collaborating on a problem-solving task. Our findings reveal that more dimensions and communication channels, in addition to space, might strongly impact territoriality and territorial behavior in hybrid collaboration. Besides classical spatial territories also auditory territories frequently emerged. In addition, visibility of and accessibility to certain territories need to be rethought. We discuss these novel findings also regarding their interplay with earlier ones and derive design implications for CSCW systems supporting hybrid collaboration.

With the vision of Industry 4.0 and cobots, working conditions in industrial settings are starting to change. We review related literature from the fields of human-robot interaction, work and organizational psychology, and sociology of work, as well as an exemplary project case study, and identify research gaps regarding the implications of cobots for work environments. We argue that we are in a transition phase from automation to actual collaboration with robots in manufacturing, and that this will open up a new problem space for investigations, in which a practice lens will be crucial. Based on this, we propose a research agenda for social practice and workplace studies to explore the sociotechnical environment of Industry 4.0 involving cobots at the individual, team, and organizational levels.