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.

The increasing integration of human-robot interaction (HRI) into social settings demands the development of human-centered AI technologies that prioritize intuitive, ethical, and empathetic interactions. As robots become more prevalent in everyday life—ranging from assistive devices in healthcare to educational tools in classrooms and customer service agents in retail—it is essential to ensure they can communicate and collaborate with humans in ways that are not only effective but also socially appropriate and meaningful. This workshop aims to explore cutting-edge advancements and interdisciplinary approaches to building AI-driven systems that facilitate effective, meaningful, and socially appropriate interactions between robots and humans across various environments such as healthcare, education, and customer service. We will primarily focus on several key themes, such as human-centered contextual AI, AI-driven intelligent robotics, ethical and responsible AI, and real-world applications. This workshop invites contributions from researchers, practitioners, and developers who are working on AI systems that empower robots to operate effectively in human-centered environments. By addressing challenges such as interpreting human emotions, understanding social cues, and adhering to ethical standards, and by sharing advancements in human-centered AI, we aim to shape the future of HRI. Our goal is to ensure that robots enrich human social experiences, fostering interactions that are not only efficient but also enhance the quality of life. By uniting efforts from various disciplines, we aspire to create robots that seamlessly integrate into society, ultimately contributing to a more harmonious coexistence between humans and robotic systems.

For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed. Current systems broadly fall into two categories – image-based which are accurate, but lack privacy, and RF signal-based, which preserve privacy but lack portability, compactness and accuracy. Our paper proposes mmSense, an end-to-end portable miniaturised real-time system that can accurately detect the presence of concealed metallic objects on persons in a discrete, privacy-preserving modality. mm-Sense features millimeter wave radar technology, provided by Google’s Soli sensor for its data acquisition, and TransDope, our real-time neural network, capable of processing a single radar data frame in 19 ms. mmSense achieves high recognition rates on a diverse set of challenging scenes while running on standard laptop hardware, demonstrating a significant advancement towards creating portable, cost-effective real-time radar based surveillance systems.

Robots play a vital role in modern automation, with applications in manufacturing and healthcare. Collaborative robots integrate human and robot movements. Therefore, it is essential to ensure that interactions involve qualified, and thus identified, individuals. This study delves into a new approach: identifying individuals through robot arm movements. Different from previous methods, users guide the robot, and the robot senses the movements via joint sensors. We asked 18 participants to perform six gestures, revealing the potential use as unique behavioral traits or biometrics, achieving F1-score up to 0.87, which suggests direct robot interactions as a promising avenue for implicit and explicit user identification.

Research in human-robot collaboration explores aspects of using interaction modalities and their effect on human perception. Particular attention is paid to intent communication, which is essential for successful interaction and collaboration. This work investigates the effect of using audio, visual, and haptic feedback on intent communication in a human-robot collaboration task where the collaborators do not share a direct line of sight. A user study was conducted in virtual reality with 20 participants. Qualitative and quantitative feedback was collected from all participants. When compared with a baseline of no feedback given to the participants, results show that using visual feedback had a significant impact on task efficiency, user experience, and cognitive load. Audio feedback was slightly less impactful, while haptic feedback had a divisive effect. Multimodal feedback combining the three modalities showed the highest impact compared to the individual modalities, leading to the highest task efficiency and user experience, and the lowest cognitive load.

Most of the current adaptive systems support single task activities. The rise in the number of daily interactive devices and sources of information made multitasking an integral activity in our daily life. Affect-aware systems show exciting potential to support the user, however, they focus on the induced effect of an additional task in terms of cognitive load and stress, rather than the influence of the number of tasks i.e. multitasking. This paper presents indicators of the number of tasks being performed by the user using a set of bio-sensors. A preliminary user study was conducted with two follow-up explorations. Our findings imply that we can distinguish between the number of tasks performed based on high-end as well as cheap Heart Rate sensors. Additionally, tasks number correlates with other signals, namely wrist and forehead temperature. We provide empirical evidence showing how to differentiate between single- and dual-tasking activities.

Most of the typical digital systems are not fully aware of the users' affect states. Adapting to the users' state showed great potential for enhancing user experiences. However, most approaches for sensing affective states, specifically arousal and valence, involve expensive and obtrusive technologies, such as physiological sensors attached to users' bodies. This paper present an indicator of the users' affect based on eye tracking. We use a commercial eye tracker to monitor the user's pupil size to estimate their arousal and valence in response to videos of different content. To assess the effect of different content (namely pleasant and unpleasant) influencing the arousal and valence on the pupil diameter, we conducted a user study with 25 participants. The study showed that different content of videos affect the pupil diameter, thereby giving an indicator about the user's state. We provide empirical evidence showing how to unobtrusively detect changes in users' state. Our initial investigation gives rise to eye-based user's tracking, which introduces the potential of new applications in the field of affect-aware computing.