Recent research has demonstrated the potential for representing intelligent guidance using multi-modal cues, yet few guidelines or processes exist to guide the design of such a system. In this article, we seek to address this gap by investigating the design of multi-modal assistant systems for setting the optimal parameters in industrial plants. We present the results of two user studies conducted with a total of 37 participants to evaluate the effectiveness and experience of different combinations of visual ( Highlights and Ambient lights) and haptic ( Clicks and Vibration ) modalities for providing intelligent dynamic guidance. Our findings demonstrate that providing the intelligent guidance with the multi-modality of Highlights+Ambient resulted in shorter task duration and higher practicality than Ambient lights alone. Regarding task accuracy, the Highlights and Vibration outside-zone guidance resulted in no failed attempts. Moreover, Highlights+Ambient+Vibration guidance was rated as having lower usability than Highlights+Ambient , and higher mental demand than merely Highlights .

The integration of humans and robots in real-world scenarios requires the development of cooperative environments where seamless collaboration is supported by intuitive user interfaces and calibrated trust. This paper reports on a virtual reality user study (N = 22) conducted in the context of collaborative interaction with a semi-autonomous robotic forklift. In the study, we investigated the relationship between perceived user experience of a human-robot interface and trust, as well as factors influencing users’ choices between two error-handling methods (manual vs. automatic recalibration) to solve a system failure. No differences in terms of trust attribution emerged between error-handling options. However, choosing the manual control yielded higher pragmatic quality of user experience and satisfaction of competence need ratings. Positive correlations between trust, user experience, and needs satisfaction were also found. Our results offer insights into user experience and trust-building in industrial human-robot interaction, shedding light on the critical role of human factors in designing effective automation systems.

Transportation significantly contributes to CO2 emissions and thus plays a crucial role in sustainability initiatives. Promoting sustainable mobility behaviors requires innovative approaches, including the use of persuasive technologies. While such strategies have shown potential, their effectiveness across different stages of the travel booking process remains underexplored. In this work, we aimed to explore how integrating persuasive strategies into the booking application of an on-demand mobility service promotes sustainable transportation choice and behavior. To accomplish this, we conducted a between-subjects user study (N=30) which encompassed a booking simulation experiment which visualized CO2 emissions feedback at two different stages of the booking process. Our results show higher persuasion score in post-booking compared to pre-booking group while no significant differences were observed in persuasive potential or engagement.

Leveraging gesture-based controls within a remote interface for automated material handling operations presents a promising avenue to optimize experience in outdoor environments. We present a gesture-based interactive system designed to facilitate collaboration between a human operator and a semi-autonomous forklift in material handling processes. Our interface supports commands that include starting the forklift, defining the loading/unloading area and the amount of pallets that need to be transported. We tested and compared the proposed solution to a touch-based User Interface in a field study to explore its effectiveness and identify potential challenges for implementation and adoption in industrial real-world settings.

Recent research has demonstrated the potential for representing intelligent guidance using multi-modal cues, yet few guidelines or processes exist to guide the design of such a system. In this paper, we seek to address this gap by investigating the design of multi-modality assistant systems for setting the optimal parameters in industrial plants. We present the results of our study conducted with 22 participants to evaluate the effectiveness and experience of different combinations of visual (Highlights and Ambient lights) and haptic (Clicks and Vibration) modalities for providing intelligent dynamic guidance. Our findings demonstrate that providing the intelligent guidance with the multi modality of Highlights+Ambient resulted in shorter task duration and higher practicality than Ambient lights alone. Moreover, Highlights+Ambient+Vibration guidance was rated with lower usability than Highlights+Ambient as well as higher mental demand than merely Highlights.

Investigating application areas for utilizing robots to support human workers is a continuing concern within human robot collaboration. In this paper, we focus on surface repair and finishing processes in which robots support skilled workers in task execution. We conducted a user study which investigates novel, pen-based human robot interaction modalities for collaborative spot repair processes in a Virtual Reality (VR) setting. Our findings show that participants preferred a test condition in which all interaction with the robot took place directly on the work piece. Furthermore, the interaction modality had an impact on objective performance indicators. These findings are discussed in the context of designing intuitive interfaces for collaborative robots in industrial settings.

Collaborative industrial robotic arms (cobots) are integrated industrial assembly systems relieving their human coworkers from monotonous tasks and achieving productivity gains. The question of task allocation arises in the organization of these human–robot interactions. State of the art shows static, compensatory task allocation approaches in current assembly systems and flexible, adaptive task sharing (ATS) approaches in human factors research. The latter should exploit the economic and ergonomic advantages of cobot usage. Previous research results did not provide a clear insight into whether industrial workers prefer static or adaptive task allocation and which tasks workers do prefer to assign to cobots. Therefore, we set up a cobot demonstrator with a realistic industrial assembly use case and did a user study with experienced workers from the shop floor (n = 25). The aim of the user study is to provide a systematic understanding and evaluation of workers' preferences in a practical context of human–robot interaction (HRI) in assembly. Our main findings are that participants preferred the ATS concept to a predetermined task allocation and reported increased satisfaction with the allocation. Results show that participants are more likely to give manual tasks to the cobot in contrast to cognitive tasks. It shows that workers do not entrust all tasks to robots, but like to take over cognitive tasks by themselves. This work contributes to the design of human-centered HRI in industrial assembly systems.

Robots need to understand words for references in social spaces (e.g., objects, locations, actions). Grounded language learning systems aim to learn these words from observing a human tutor. Teaching a robot is difficult for naive users due to the discrepancy between the users' mental model and the actual state of the robot. We present a grounded word-learning system with the Pepper robot which learns object and action labels and investigate two extensions geared towards increasing the system’s transparency. The first extension utilizes deictic gestures (pointing and gaze) to communicate knowledge about object names and to further request new labels. The second extension shows the current state of the lexicon on the robot’s tablet. We performed a user study (n=32) to investigate the effects of the transparency methods on learning performance and teaching behavior. In a quantitative analysis, we did not see a significant performance increase for the two extensions. However, users reported higher perception of control and perceived learning success the better they knew the current state of the learning system. In a qualitative analysis, we investigated the participants' teaching behaviors and identified factors that inhibited the learning process. Among other things, we found increased interactive behavior of users when the robot displayed deictic gestures. We saw that human tutors simplified their utterances over time to adapt to the perceived capabilities of the robot. The tablet was most helpful for users to understand what the robot had already learned. Still, learning was impaired in all conditions, when the human input substantially deviated from the form required by the learning system.

People form mental models about robots' behavior and intention as they interact with them. The aim of this paper is to evaluate the effect of different interaction styles on self-efficacy in human-robot interaction (HRI), people's perception of the robot, and task engagement. We conducted a user study in which a social robot assists people verbally while building a house of cards. Data from our experimental study revealed that people engaged longer in the task while interacting with a robot that provides person related feedback than with a robot that gives no person or task related feedback. Moreover, people interacting with a robot with a person-oriented interaction style reported a higher self-efficacy in HRI, perceived higher agreeableness of the robot and found the interaction less frustrating, as compared to a robot with a task-oriented interaction style. This suggests that a robot's interaction style can be considered as a key factor for increasing people's perceived self-efficacy in HRI, which is essential for establishing trust and enabling Human-robot collaboration.