Museums aim at personalizing visits to offer tailored content and encourage visitors to come back. Unfortunately, museum professionals find it difficult to reflect on the vast number of visitor profiles when creating personalized visits. We propose Museo* a new concept to support their creation strategy by allowing the selection of visitor characteristics. Through an interactive design process, we instantiated this concept with MuseoTUI, that displays the progress of visit creation by illuminating physical tokens, and MuseoGUI that displays on a standard touchscreen. In an in-situ comparative structured observation, we evaluated both prototypes. The concept Museo* is well understood and accepted with both interaction styles. MuseoGUI was perceived as more efficient, while MuseoTUI provided better stimulation, higher user experience and the physical manipulation was described as a valuable support for empathy, reflection, and creation. Based on these findings, we present design implications for future systems supporting the creation of personalized visits.

Ensuring the safety of personnel at large industrial sites is a significant challenge. Misunderstandings and increased safety risks are related to communication, as traditional non-targeted alerts like sirens are often ineffective. To address this, we propose a drone-based system that uses voice interaction to deliver timely and targeted safety advisories. This paper presents a multi-stage, user-centered study, beginning with formative interviews with security and safety managers and culminating in an on-site evaluative study with personnel at two biomass power plants. Through our study, we identify key design insights for creating a drone that is perceived as an official, authoritative, and non-threatening entity. We provide design guidance for using sound and movement to signal intent, creating a voice persona that balances authority with clarity, and designing interaction flows to manage confusion and non-compliance. The results demonstrate the system's potential not only to enhance safety compliance, but also to mitigate interpersonal confrontation. First, we provide an empirically-grounded understanding of contextual safety challenges, which informs a set of design principles for intelligible and socially acceptable voice advisories from drones. Second, we present results from an in-situ evaluation of a working prototype that demonstrates its effectiveness in a real-world setting.

Human-Drone Interaction (HDI) research focuses on multi-rotors, despite the efficiency and range advantages of Fixed-Wing Drones (FWDs) critical for large-scale disaster response. FWD adoption in HDI is hindered by the stall constraint–the necessity of constant forward motion. This paper introduces Human-Fixed-Wing Interaction (HFWI), a design space that reframes continuous motion from a liability into a communicative asset. Through a scenario-based elicitation study with 37 aeronautically literate participants, we investigated how native flight dynamics and metaphors can form a legible kinesic vocabulary without requiring external hardware interfaces. Our findings reveal a Degree-of-Freedom (DoF) Hierarchy, where low-complexity maneuvers communicate benign intent, while high-complexity aerodynamics signal emergency or refusal. We further identify distinct communicative affordances, specifically the Perimeter of Attention—where continuous orbiting enables scalar feedback to define spatial boundaries—and the superior legibility of Sequential Gestures. We conclude that FWDs provide a foundational grammar for interaction with long-endurance aerial platforms.

Drones are increasingly being deployed to assist firefighting crews in their missions, with the technology being chosen based on availability, rather than aligned with their specific needs. This phenomenon is exacerbated in the Global South, where infrastructure is scarce and where specific processes and user needs have to be adequately mapped to successfully introduce new technologies. We conducted semi-structured interviews with firefighting professionals (N=15) from Thailand, covering their prior experience with drones, challenges they encounter in their job, and how they envision this technology could better support them in the future. Our findings describe users’ technological needs and their expectations in terms of interaction and collaboration with drones. We identified specific challenges in Thailand that hinder the deployment of drone technology, including mismatches in technical and financial decisions. Furthermore, participants advocated for sharing physical systems between fire departments. We conclude with design considerations for drones in resource-limited firefighting contexts.

Uncrewed Aerial Systems (UAS) mishaps are partially attributable to mental fatigue during long endurance missions. Mental fatigue decreases cognitive flexibility resulting in a decreased performance when switching between sub-tasks. Solutions include adaptive interfaces. We propose a technical proof of concept that launches visual alerts to mitigate operator mental fatigue by estimating future performance based on brain activity (EEG). The proposed solution uses flying and detection tasks, and includes an online classification pipeline. The method is designed to work in an intra-user and inter-session manner, i.e. the solution is tuned to a user and works without calibration in the following sessions. The proof of concept was validated on three participants and compared with a previous campaign with randomly launched alerts. Very promising results were found (negative correlation between visual alerts activation and detection misses). This opens the way for the use of physiology-based adaptive interfaces to enhance user performance and safety.

Artificial Intelligence and Robotics are trending domains these days. Week after week, there are new impressive videos of robots who are walking, running or somersaulting, and of AI agents performing complex tasks. Such achievements, in addition to all that is happening in related areas, could make us think that the introduction of robots into our daily lives may happen, if not tomorrow, so in the near future. Our experiences as researchers in a robotics research laboratory suggest otherwise. But technological progress is difficult to predict and regardless of when it will happen, it is important to prepare and be prepared for it. We argue that it is important to pursue interdisciplinary fundamental research on how to place the Human at the heart of our research concerns in engineering and computer science. In this paper, we propose and sketch a holistic approach to Artificial Intelligence and Robotics FOR, AMONG, WITH and BY Humans.

Objective: A central component of search and rescue missions is the visual search of survivors. In large parts, this depends on human operators and is, therefore, subject to the constraints of human cognition, such as mental fatigue (MF). This makes detecting MF a critical step to be implemented in future systems. However, to the best of our knowledge, it has seldom been evaluated using a realistic visual search task. In addition, an accuracy discrepancy exists between studies that use time-on-task (TOT)-the popular method-and performance metrics for labels. Yet, to our knowledge, they have never been directly compared. Approach: This study was designed to address both issues: the use of a realistic task to elicit MF during a monotonous visual search task and the labeling type used for intra-participant fatigue estimation. Over four blocks of 15 min, participants had to identify targets on a computer while their cardiac, cerebral (EEG), and eye-movement activities were recorded. The recorded data were then fed into several physiological computing pipelines. Main results: The results show that the capability of a machine learning algorithm to detect MF depends less on the input data but rather on how MF is defined. Using TOT, very high classification accuracies are obtained (e.g. 99.3%). On the other hand, if MF is estimated based on behavioral performance, a metric with a much greater operational value, classification accuracies return to chance level (i.e. 52.2%). Significance: TOT-based MF estimation is popular, and strong classification accuracies can be achieved with a multitude of sensors. These factors contribute to the popularity of this method, but both usability and the relation to the concept of MF are neglected.

Recent progress in aerial robotics foresees that flying robots, a.k.a. drones, can support workers in their jobs, such as by performing complex tasks in hard-to-reach places. As they become increasingly autonomous, we envision co-working drones helping human operators in direct collaborative tasks, such as by carrying tools and handing them over to workers at heights, or helping them lift and precisely position structures on construction sites. Yet, much research is needed to support safe close-body interaction between humans and drones. We here propose specific considerations for human-drone collaboration related to such handover, from the drone approaching a person in view of interacting with them at close proximity, to the handover itself, and to the drone leaving. In addition, we present the results of semi-structured interviews with three professionals in this context of human-drone collaboration. This late-breaking report highlights challenges and opportunities fostered by Human-Aerial Robot Handover (HARH).

Similarity between tasks is an understudied factor in research on cognitive flexibility. This behavioural experiment had 31 participants perform a task switch paradigm in which participants were required to switch between 4 tasks of varying similarity. The experiment was constructed in a way that simultaneously allows for investigating the impact of mental fatigue and task-rule congruency on the participants. The results indicate that similarity between tasks substantially impacts performance with different effects on RT and accuracy. While learning effects may have negated the impact of mental fatigue across the 5 experimental blocks, a significant decrease in performance was observed within blocks. Furthermore, the exploratory analysis proposes a novel interaction between task-rule incongruent trials and the task of the previous trial. These results support the notion that neither the interference view of cognitive flexibility nor the reconfiguration view are fully adequate at explaining task switch costs if similarity is added as a factor. The presented study presents strong evidence that fundamental findings in the domain of cognitive flexibility may not map linearly to more ecological settings where tasks are often more dissimilar.

There is a wide diversity of platforms for teleoperating robots. Every robot, use case, and type of user brings a unique set of expectations. We propose that human-robot interaction should consider the opportunity to use an external device (such as a smartphone) to not only teleoperate but also mediate the interaction with a robot, either in telepresence or in co-presence. In this paper, we present first steps and ideas towards the development of this media. It involves, as first end-users, the members of the robotics department of our laboratory and considers all robotic platforms which are hosted there (assistance robots, terrestrial robots, robots humanoids, quadrupeds, etc.). We describe our user-centred design methodology, detailing the needs analysis and brainstorming sessions conducted. Following this, we outline the design and prototyping phases, showcasing the iterative development of our interface. Finally, we discuss the results from our evaluations and the implications for future work in creating flexible and inclusive interfaces for diverse user groups.

Autopilot interfaces are designed to help aircrews maintain awareness about the aircraft’s current automation mode, the lack of which can lead to fatal accidents. However, even though researchers have identified multiple design flaws, cockpit designs remain largely unchanged. We first review the research literature on autopilot design, with a special emphasis on addressing mode awareness in order to create a design space that characterizes autopilot interfaces. We next follow a generative design approach that analyzes and critiques three existing commercial autopilot interfaces with respect to this design space so as to identify their gaps and limitations. We then extend the design space to include additional design dimensions and conclude by suggesting directions for improving future autopilots.

This study investigates the mitigation of cognitive flexibility decrements during long-endurance Uncrewed Aerial Systems (UAS) missions through visual alerts. UAS mishaps are likely partially attributable to decreased cognitive flexibility caused by mental fatigue. Two groups of participants performed a 2-hour UAS simulation. One group was supported by visual alerts when switching between tasks, while the other was not. To further investigate the effect of visual alerts, the participant’s cerebral and cardiac activity during the task was recorded using electroencephalography (EEG) and electrocardiography (ECG). Results showed a weaker subjective increase of mental fatigue across time with visual alerts than the control group. Furthermore, behavioural results showed improved reaction time and accuracy in some performance metrics. Future work may improve this system’s efficacy by implementing it with an adaptive interface.

Mental fatigue from continuous operations without breaks represents a safety issue for military drone operations, as these systems are complex and operate during long shifts. Military operations are hard to study due to their sensitive nature. The open-access program UASOS serves as a testbed to examine the effects of mental fatigue in an ecologically valid environment. UASOS recreates fundamental aspects of military drone operations in a controllable environment that is easy enough for novices to understand but demanding enough to elicit mental fatigue. Participants alternate between navigating a drone-using either a trackball/mouse or a joystick-and searching for visual targets. The protocol is set up in a way that taxes the cognitive flexibility of participants by constantly requiring them to alternate between tasks. In addition, several parameters such as difficulty, duration, questionnaires, training phases, and more can be adapted. The task also allows for synchronization with physiological data using LabStreamingLayer. Implemented in python, the code is set up to be easily installed.

Autopilot interfaces are designed to help aircrews maintain awareness about the aircraft's current automation mode, the lack of which can lead to fatal accidents. However, even though researchers have identified multiple design flaws, cockpit designs remain largely unchanged. We first review the research literature on autopilot design, with a special emphasis on addressing mode awareness in order to create a design space that characterizes autopilot interfaces. We next follow a generative design approach that analyzes and critiques three existing commercial autopilot interfaces with respect to this design space so as to identify their gaps and limitations. We then extend the design space to include additional design dimensions and conclude by suggesting directions for improving future autopilots.

Mode confusion and automation surprises in aviation raise questions about the design of flight deck interfaces. Prior research investigated the use of the current interfaces and how they can impact the pilot’s awareness of modes, and proposed design solutions to reduce mode confusions by improving feedback and interaction with modes. This paper explores a novel design that brings together mode control and feedback in a single interface. The interface aims to reduce mode confusions. Moreover, the paper highlights 5 key dimensions that influenced the design. In the future, we intend to evaluate the proposed interface to validate its benefits.