Knowing what collaborators attend to is essential. Previous studies demonstrated that shared gaze enhances coordination and social connectedness in remote settings. In collocated settings, gaze can be both naturally observable and technologically augmented. AR enables gaze cues to be rendered explicitly in the environment. To investigate if and how such cues are beneficial in collocated AR collaboration, we examined both qualitative and quantitative effects across three task types (puzzle, negotiation, search) and two spatial setups (plane, room), focusing on task completion time and the collaborative experience. In our user study with 24 dyads (n=48), we varied gaze visibility and measured task performance, user preference, social connectedness, and shared attention. Our results show that sharing gaze in collocated collaborative AR can increase shared attention, is perceived as helpful, and improves the user experience, similar to remote collaboration, but has a limited impact on the actual task completion time across the chosen tasks.

Human–robot collaboration faces a speed–accuracy trade-off (SAT): higher speed lowers latency but increases errors; lower speed improves accuracy but extends waiting time. Both pathways can frustrate users in research and real-world deployments. Despite this importance, the impact of SAT on frustration and how to mitigate it remains underexplored. We conducted a user study (N=24) in which participants collaborated with a robot in an assembly task. We investigate three levels of SAT (conservative, moderate, risky), and examine how uncertainty communication and offering decision autonomy affect user frustration. Our results show that user frustration is highest for risky robot behavior. User involvement decreased frustration for risky behaviors, but increased it for conservative ones, while verbal uncertainty communication had no effects. We further found that perceived transparency, agency, intelligence, and utility of the robot increase with conservative SAT, with user workload decreasing. We propose that user involvement is advisable in higher-risk settings to mitigate user frustration, whereas autonomous operation is preferable in lower-risk scenarios.

There seems to be a solid object inside.""What other toppings do you usually like on your pizza?" Figure 1: We imbued a robot with curious behaviors.The figure shows two examples.Left: The robot shakes a container to check whether there is an object inside.Right: The robot asks for the person's preferences.

Like humans, today's systems, such as robots and voice assistants, can express curiosity to learn and engage with their surroundings.While curiosity is a well-established human trait that enhances social connections and drives learning, no existing scales assess the perceived curiosity of systems.Thus, we introduce the Perceived System Curiosity (PSC) scale to determine how users perceive curious systems.We followed a standardized process of developing and validating scales, resulting in a validated 12-item scale with 3 individual sub-scales measuring explorative, investigative, and social dimensions of system curiosity.In total, we generated 831 items based on literature and recruited 414 participants for item selection and 320 additional participants for scale validation.Our results show that the PSC scale has inter-item reliability and convergent and construct validity.Thus, this scale provides an instrument to explore how perceived curiosity influences interactions with technical systems systematically.

Figure 1: The two-phase process for eliciting and verifying gestures.

Human-human handovers are natural and seamless. To be able to do this, humans optimize towards many factors. One of them is the timing when receiving an object. However, the preferred robot reaction time in Human-Robot handovers is currently unclear. To understand the preferred robot reaction time, we trained an Space-Time-Separable Graph Convolutional Network (STS-GCN) model using motion capture data of human-human handovers. We deployed this system on a robotic arm with live depth camera data. We conducted a user study (N=20) with five robot reaction times. We found that users perceived an early prediction as preferred. Furthermore, we found that designers can adapt this timing to their needs based on six sub-components of user perception. We contribute a ready-to-deploy hand over classification model, a preferred handover time for our system, and an approach to determine the preferred robot reaction time for robotic systems.

The vision of adaptive architecture proposes that robotic technologies could enable interior spaces to physically transform in a bidirectional interaction with occupants. Yet, it is still unknown how this interaction could unfold in an understandable way. Inspired by HRI studies where robotic furniture gestured intents to occupants by deliberately positioning or moving in space, we hypothesise that adaptive architecture could also convey intents through gestures performed by a mobile robotic partition. To explore this design space, we invited 15 multidisciplinary experts to join co-design improvisation sessions, where they manually manoeuvred a deactivated robotic partition to design gestures conveying six architectural intents that varied in purpose and urgency. Using a gesture elicitation method alongside motion-tracking data, a Laban-based questionnaire, and thematic analysis, we identified 20 unique gestural strategies. Through categorisation, we introduced architectonic gestures as a novel strategy for robotic furniture to convey intent by indexically leveraging its spatial impact, complementing the established deictic and emblematic gestures. Our study thus represents an exploratory step toward making the autonomous gestures of adaptive architecture more legible. By understanding how robotic gestures are interpreted based not only on their motion but also on their spatial impact, we contribute to bridging HRI with Human-Building Interaction research.

As autonomous agents, from self-driving cars to virtual assistants, become increasingly present in everyday life, safe and effective collaboration depends on human understanding of agents’ intentions. Current intent communication approaches are often rigid, agent-specific, and narrowly scoped, limiting their adaptability across tasks, environments, and user preferences. A key gap remains: existing models of what to communicate are rarely linked to systematic choices of how and when to communicate, preventing the development of generalizable, multi-modal strategies. In this paper, we introduce a multidimensional design space for intent communication structured along three dimensions: Transparency (what is communicated), Abstraction (when), and Modality (how). We apply this design space to three distinct human-agent collaboration scenarios: (a) bystander interaction, (b) cooperative tasks, and (c) shared control, demonstrating its capacity to generate adaptable, scalable, and cross-domain communication strategies. By bridging the gap between intent content and communication implementation, our design space provides a foundation for designing safer, more intuitive, and more transferable agent-human interactions.

Passive voice assistants such as Alexa are widespread, responding to user requests. However, due to the rise of domestic robots, we envision active smart assistants initiating interactions seamlessly, weaving themselves into the user’s context, and enabling more suitable interaction. While robots already deliver the hardware, only recently have the advancements in artificial intelligence enabled assistants to grasp the human and the environments to support such visions. We combined hardware with artificial intelligence to build an attentive robot. Here, we present a robotic head prototype discovering and following the users in a room supported by video and sound. We contribute (1) the design and implementation of a prototype system for an active smart assistant and (2) a discussion on design principles for systems engaging in human conversations. This work aims to provide foundations for future research for active smart assistants.