Multilingual collaboration is increasingly common as today’s world becomes global and culturally diverse. While diversity fosters innovation, language barriers can hinder involvement and effective communication. Prior work has primarily focused on improving translation accuracy with limited attention to how translation systems shape dimensions of trust in interaction. Given that users must rely blindly on technology due to their inability to understand the system’s output, this issue becomes a crucial aspect. To address this gap, we introduce MorphHat, a co-embodied humanoid robot interpreter featuring a customizable, morphing face that visually represents the active speaker. We evaluated MorphHatMorphHat influenced trust, rapport, and social presence. We discuss these insights as early design implications for future embodied translation systems.

Knowledge workers increasingly rely on multi-agent systems to solve complex problems. While these systems offer valuable support, they often obscure which agents contributed to a response, leading to a lack of transparency that may result in errors and reduced trust. To address this, we propose avatars that make agents’ expertise and contributions transparent. We iteratively co-designed avatars representing distinct expertise areas and validated them in an experiment (N=100). Building on this, we developed four multi-agent prototypes varying in explanation modality (text vs. avatars) and resolution (low vs. high). We then conducted a mixed-methods evaluation with an online experiment (N=124) and follow-up interviews (N=20). Qualitative results suggest that avatars foster clearer mental models, improve perceived explainability, and support users’ trust calibration without increasing cognitive load, although no significant quantitative differences were found. Our research contributes validated avatar designs, insights into explanation strategies, and design implications for explainable multi-agent systems.

Cognitive empathy, the ability to understand others‘ perspectives, is essential for effective communication, reducing biases, and constructive negotiation. However, this skill is declining in a performance-driven society, which prioritizes efficiency over perspective-taking. Here, the training of cognitive empathy is challenging because it is a subtle, hard-to-perceive soft skill. To address this, we developed CoEmpaTeam, a VR-based system that enables users to train their cognitive empathy by using LLM-driven avatars with different personalities. Through dynamic role play, users actively engage in perspective-taking, experiencing situations through another person’s eyes. CoEmpaTeam deploys three avatars who significantly differ in their personality, validated by a technical evaluation and an online experiment (n=90). Next, we evaluated the system through a lab experiment with 32 participants who performed three sessions across two weeks, followed by a one-week diary study. Our results showed a significant increase in cognitive empathy, which, according to participants, transferred into their real lives.

Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners’ attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners’ reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.

Virtual reality (VR) enables immersive, tightly controlled experiments but complicates subjective measurement: moving participants out of the virtual environment (VE) to complete questionnaires changes the measurement context and can increase recall bias. Questionnaires embedded in the VE $({}_{IN} \text{VRQs})$ enable immediate, time-efficient self-reports and repeated sampling, yet adoption is limited by concerns that ${}_{IN} \text{VRQs}$ might bias primary experiential measures and by the implementation burden of usable interfaces. In this within-subject study, 43 participants completed post-task questionnaires using three representative 2D ${}_{IN} \text{VRQ}$ interface designs: a body-anchored watch (direct touch), a world-anchored station (handheld pointer), and a world-anchored board at 5 m (laser pointer). Usability and task load differed significantly across interfaces, revealing clear design trade-offs. Interview feedback highlighted corresponding ergonomic themes. In contrast, ratings of presence and flow related to the constant task and VE showed no practically relevant differences across interfaces, supported by equivalence tests. Findings suggest that once basic usability requirements are met, interface choice is unlikely to meaningfully bias experiential measures in similar setups. This study contributes methodological guidance for implementing or selecting ${}_{IN} \text{VRQ}$ tools to improve VR study quality, comparability, and reproducibility. Supplemental materials are available at osf.io/528yk (CC BY 4.0).

CHI ’25, Yokohama, Japan

To enhance interactions in VR, many techniques introduce offsets between the virtual and real-world position of users’ hands. Nevertheless, such hand redirection (HR) techniques are only effective as long as they go unnoticed by users—not disrupting the VR experience. While several studies consider how much unnoticeable redirection can be applied, these focus on mid-air floating hands that are disconnected from users’ bodies. Increasingly, VR avatars are embodied as being directly connected with the user’s body, which provide more visual cue anchoring, and may therefore reduce the unnoticeable redirection threshold. In this work, we studied more complete avatars and their effect on the sense of embodiment and the detectability of HR. We found that higher avatar completeness increases embodiment, and we provide evidence for the absence of practically relevant effects on the detectability of HR.

In pursuit of hand redirection techniques that are ever more tailored to human perception, we propose the first algorithm for hand redirection in virtual reality that makes use of saccades, i.e., fast ballistic eye movements that are accompanied by the perceptual phenomenon of change blindness. Our technique combines the previously proposed approaches of gradual hand warping and blink-suppressed hand redirection with the novel approach of saccadic redirection in one unified yet simple algorithm. We compare three variants of the proposed Saccadic & Blink-Suppressed Hand Redirection (SBHR) technique with the conventional approach to redirection in a psychophysical study (N = 25). Our results highlight the great potential of our proposed technique for comfortable redirection by showing that SBHR allows for significantly greater magnitudes of unnoticeable redirection while being perceived as significantly less intrusive and less noticeable than commonly employed techniques that only use gradual hand warping.

Enabling multi-fingered robots to choose an appropriate grasp on an object from natural language instructions poses great difficulties for such systems. The diversity, imprecision, and limited information contained in the language make this task particularly challenging. However, speech serves humans as a natural communication interface that can aid robots in adapting to the environment more easily. Therefore, providing robots with relevant data about the objects they interact with is essential for them to understand how to carry out object manipulation tasks. By leveraging Named Entity Recognition (NER) to automatically extract semantic data, our work introduces a novel approach to text-based grasp predictions. Our methodology involves a multistage learning approach using a semantic information extractor that provides significant features to a grasp prediction model. To assess the effectiveness of our approach, we conducted experiments on an existing corpus and two corpora generated by ChatGPT. Our results demonstrate superior performance compared to similar grasp prediction models while overcoming limitations in the literature. Additionally, we open-source our training data for reproducibility and future research advancement.

Robots that make use of multi-fingered or fully anthropomorphic end-effectors can engage in highly complex manipulation tasks. However, the choice of a suitable grasp for manipulating an object is strongly influenced by factors such as the physical properties of an object and the intended task. This makes predicting an appropriate grasping pose for carrying out a concrete task notably challenging. At the same time, current grasp type predictors rarely consider the task as a part of the prediction process. This work proposes a learning model that considers the task in addition to an object’s visual features for predicting a suitable grasp type. Furthermore, we generate a synthetic dataset by simulating robotic grasps on 3D object models based on the BarrettHand end-effector. With an angular similarity of 0.9 and above, our model achieves competitive prediction results compared to grasp type predictors that do not consider the intended task for learning grasps. Finally, to foster research in the field, we make our synthesized dataset available to the research community.

Many illusion and interaction techniques in Virtual Reality (VR) rely on Hand Redirection (HR), which has proved to be effective as long as the introduced offsets between the position of the real and virtual hand do not noticeably disturb the user experience. Yet calibrating HR offsets is a tedious and time-consuming process involving psychophysical experimentation, and the resulting thresholds are known to be affected by many variables—limiting HR’s practical utility. As a result, there is a clear need for alternative methods that allow tailoring HR to the perceptual boundaries of individual users. We conducted an experiment with 18 participants combining movement, eye gaze and EEG data to detect HR offsets Below, At, and Above individuals’ detection thresholds. Our results suggest that we can distinguish HR At and Above from no HR. Our exploration provides a promising new direction with potentially strong implications for the broad field of VR illusions.

Experiencing virtual environments is often limited to abstract interactions with objects. Physical proxies allow users to feel virtual objects, but are often inaccessible. We present the VoxelHap toolkit which enables users to construct highly functional proxy objects using Voxels and Plates. Voxels are blocks with special functionalities that form the core of each physical proxy. Plates increase a proxy’s haptic resolution, such as its shape, texture or weight. Beyond providing physical capabilities to realize haptic sensations, VoxelHap utilizes VR illusion techniques to expand its haptic resolution. We evaluated the capabilities of the VoxelHap toolkit through the construction of a range of fully functional proxies across a variety of use cases and applications. In two experiments with 24 participants, we investigate a subset of the constructed proxies, studying how they compare to a traditional VR controller. First, we investigated VoxelHap’s combined haptic feedback and second, the trade-offs of using ShapePlates. Our findings show that VoxelHap’s proxies outperform traditional controllers and were favored by participants.

Rendering haptic feedback for interactions with virtual objects is an essential part of effective virtual reality experiences. In this work, we explore providing haptic feedback for rotational manipulations, e.g., through knobs. We propose the use of a Pseudo-Haptic technique alongside a physical proxy knob to simulate various physical resistances. In a psychophysical experiment with 20 participants, we found that designers can introduce unnoticeable offsets between real and virtual rotations of the knob, and we report the corresponding detection thresholds. Based on these, we present the Pseudo-Haptic Resistance technique to convey physical resistance while applying only unnoticeable pseudo-haptic manipulation. Additionally, we provide a first model of how C/D gains correspond to physical resistance perceived during object rotation, and outline how our results can be translated to other rotational manipulations. Finally, we present two example use cases that demonstrate the versatility and power of our approach.

Hand redirection is effective so long as the introduced offsets are not noticeably disruptive to users. In this work we investigate the use of physiological and interaction data to detect movement discrepancies between a user's real and virtual hand, pushing towards a novel approach to identify discrepancies which are too large and therefore can be noticed. We ran a study with 22 participants, collecting EEG, ECG, EDA, RSP, and interaction data. Our results suggest that EEG and interaction data can be reliably used to detect visuo-motor discrepancies, whereas ECG and RSP seem to suffer from inconsistencies. Our findings also show that participants quickly adapt to large discrepancies, and that they constantly attempt to establish a stable mental model of their environment. Together, these findings suggest that there is no absolute threshold for possible non-detectable discrepancies; instead, it depends primarily on participants' most recent experience with this kind of interaction.

On the way towards novel hand redirection (HR) techniques that make use of change blindness, the next step is to take advantage of saccades for hiding body warping. A prerequisite for saccadic HR algorithms, however, is to know how much the user’s virtual hand can unnoticeably be offset during saccadic suppression. We contribute this knowledge by conducting a psychophysical experiment, which lays the ground for upcoming HR techniques by exploring the perceptual detection thresholds (DTs) of hand offset injected during saccades. Our findings highlight the pivotal role of saccade direction for unnoticeable hand jumps, and reveal that most offset goes unnoticed when the saccade and hand move in opposite directions. Based on the gathered perceptual data, we derived a model that considers the angle between saccade and hand offset direction to predict the DTs of saccadic hand jumps.

Visuo-haptic illusions are a method to expand proxy-based interactions in VR by introducing unnoticeable discrepancies between the virtual and real world. Yet how different design variables affect the illusions with proxies is still unclear. To unpack a subset of variables, we conducted two user studies with 48 participants to explore the impact of (1) different grasping types and movement trajectories, and (2) different grasping types and object masses on the discrepancy which may be introduced. Our Bayes analysis suggests that grasping types and object masses (≤ 500 g) did not noticeably affect the discrepancy, but for movement trajectory, results were inconclusive. Further, we identified a significant difference between (un)restricted movement trajectories. Our data shows considerable differences in participants’ proprioceptive accuracy, which seem to correlate with their prior VR experience. Finally, we illustrate the impact of our key findings on the visuo-haptic illusion design process by showcasing a new design workflow.

Robotic systems using anthropomorphic end-effectors face tremendous challenges choosing a suitable pose for grasping an object. The fact that the choice of a grasp is influenced by the physical properties of an object, the intended task, and the environment results in a considerable amount of variables. The majority of models targeted towards enabling such robots to determine a suitable grasping pose rely on computer vision techniques, sometimes complemented by textual data. This paper investigates the potential of publicly available textual descriptions to predict a suitable grasping pose for anthropomorphic end-effectors. To this end, we have retrieved textual descriptions from Wikipedia, Wiktionary, and WordNet as well as a number of well-known dictionaries for 100 everyday objects. We analyze and compare the prediction quality of multiple learning methods while showing that a support vector machine-based approach can utilize this data for achieving a prediction accuracy above 0.75. Finally, we make our collected data available to the research community.

Providing haptic feedback when manipulating virtual objects is an essential part of immersive virtual reality experiences; however, it is challenging to replicate all of an object's properties and characteristics. We propose the use of visuo-haptic illusions alongside physical proxies to enhance the scope of proxy-based interactions with virtual objects. In this work, we focus on two manipulation techniques, linear translation and stretching across different distances, and investigate how much discrepancy between the physical proxy and the virtual object may be introduced without participants noticing. In a study with 24 participants, we found that manipulation technique and travel distance significantly affect the detection thresholds, and that visuo-haptic illusions impact performance and accuracy. We show that this technique can be used to enable functional proxy objects that act as stand-ins for multiple virtual objects, illustrating the technique through a showcase VR-DJ application.

Exploring and manipulating complex virtual objects is challenging due to limitations of conventional controllers and free-hand interaction techniques. We present the TanGi toolkit which enables novices to rapidly build physical proxy objects using Composable Shape Primitives. TanGi also provides Manipulators allowing users to build objects including movable parts, making them suitable for rich object exploration and manipulation in VR. With a set of different use cases and applications we show the capabilities of the TanGi toolkit and evaluate its use. In a study with 16 participants, we demonstrate that novices can quickly build physical proxy objects using the Composable Shape Primitives and explore how different levels of object embodiment affect virtual object exploration. In a second study with 12 participants we evaluate TanGi's Manipulators and investigate the effectiveness of embodied interaction. Findings from this study show that TanGi's proxies outperform traditional controllers and were generally favored by participants.

Rapid prototyping of haptic output on 3D objects promises to enable a more widespread use of the tactile channel for ubiquitous, tangible, and wearable computing. Existing prototyping approaches, however, have limited tactile output capabilities, require advanced skills for design and fabrication, or are incompatible with curved object geometries. In this paper, we present a novel digital fabrication approach for printing custom, high-resolution controls for electro-tactile output with integrated touch sensing on interactive objects. It supports curved geometries of everyday objects. We contribute a design tool for modeling, testing, and refining tactile input and output at a high level of abstraction, based on parameterized electro-tactile controls. We further contribute an inventory of 10 parametric Tactlet controls that integrate sensing of user input with real-time electro-tactile feedback. We present two approaches for printing Tactlets on 3D objects, using conductive inkjet printing or FDM 3D printing. Empirical results from a psychophysical study and findings from two practical application cases confirm the functionality and practical feasibility of the Tactlets approach.

Remote collaborators working together on physical objects have difficulty building a shared understanding of what each person is talking about. Conventional video chat systems are insufficient for many situations because they present a single view of the object in a flattened image. To understand how this limited perspective affects collaboration, we designed the Remote Manipulator (ReMa), which can reproduce orientation manipulations on a proxy object at a remote site. We conducted two studies with ReMa, with two main findings. First, a shared perspective is more effective and preferred compared to the opposing perspective offered by conventional video chat systems. Second, the physical proxy and video chat complement one another in a combined system: people used the physical proxy to understand objects, and used video chat to perform gestures and confirm remote actions.