arXiv subjects

Per Ola Kristensson

Publications and source records attributed to Per Ola Kristensson.

At least 19 recordsLinked to original sources

arXiv2026

Can People Distinguish Human and AI Agency in Humanoid Teleoperation? A Preliminary Study of Agency Perception

Can people distinguish between human and AI agency in humanoid teleoperation? To explore this question, we developed \textit{Ghost-in-the-Loop}, a teleoperation framework that supports both human-operated and AI-generated control of a robot's voice, facial expressions, and gestures while maintaining a consistent embodiment. We conducted a preliminary online study ($N=50$) in which participants viewed short interaction clips generated by either a Human Operator or an AI Control and judged the perceived source of control. Results suggest that participants often struggled to distinguish between the two conditions in brief interactions. Qualitative responses indicate that judgments were primarily influenced by perceived naturalness, temporal coordination, and consistency across speech, facial expression, and gesture. These findings provide initial insights into agency perception in embodied human--AI communication and motivate future investigations of blended human--AI telepresence systems.

cs.HC↗

arXiv2026

Evaluating the Usability of Microgestures for Text Editing Tasks in Virtual Reality

As virtual reality (VR) continues to evolve, traditional input methods such as handheld controllers and gesture systems often face challenges with precision, social accessibility, and user fatigue. These limitations motivate the exploration of microgestures, which promise more subtle, ergonomic, and device-free interactions. We introduce microGEXT, a lightweight microgesture-based system designed for text editing in VR without external sensors, which utilizes small, subtle hand movements to reduce physical strain compared to standard gestures. We evaluated microGEXT in three user studies. In Study 1 ($N=20$), microGEXT reduced overall edit time and fatigue compared to a ray-casting + pinch menu baseline, the default text editing approach in commercial VR systems. Study 2 ($N=20$) found that microGEXT performed well in short text selection tasks but was slower for longer text ranges. In Study 3 ($N=10$), participants found microGEXT intuitive for open-ended information-gathering tasks. Across all studies, microGEXT demonstrated enhanced user experience and reduced physical effort, offering a promising alternative to traditional VR text editing techniques.

cs.HC↗

arXiv2026

Extended Reality as a Mediation Layer for Situated Human Control in Human-Robot Teaming

Extended Reality (XR) is increasingly used in human-robot interaction to communicate robot intent, planned motion, reachability, and state. We argue that XR should also be understood as a mediation layer for situated human control in human-robot teaming. Situated human control denotes the human collaborator's ability to understand, shape, authorize, and interrupt robot action within the concrete physical, social, and temporal context in which that action unfolds. We ground this perspective in scenarios from robot-assisted bedside nursing, multi-arm supervisory control, and collaborative assembly under divided attention. Across these scenarios, robot autonomy must remain inspectable and adjustable as people move, goals change, sensing is incomplete, control roles shift, and plans become invalid. We identify four mediation functions connecting human intent and robot autonomy, robot plans and human judgment, levels of shared control, and team roles, handover, and recovery. Building on these functions, we derive six design dimensions: joint action possibilities, socio-physical constraints, uncertainty and plan validity, multimodal control and correction, roles, handover, and accountability, and anticipatory recovery. The paper outlines a research agenda for XR systems that make robot autonomy more actionable and accountable in dynamic shared environments.

cs.HC↗

arXiv2026

Beyond Conversations: Spatially-Anchored Previews for Intent Disambiguation in LLM-Assisted Geometry Editing in Virtual Reality

User intent disambiguation remains a key challenge in intelligent interactive systems. While they have been widely studied in dialogue systems in 2D interfaces, research on how intent disambiguation could be incorporated within Large Language Model (LLM) assisted editing workflows in immersive environments remains limited. Recent advances in LLMs create opportunities to leverage the immersive nature of virtual and augmented reality (VR/AR) environments to provide better disambiguation support. In this paper, we evaluate how traditional dialogue-based disambiguation can be augmented with spatially-anchored graphical previews to resolve ambiguous user commands in LLM-assisted parameter-driven editing workflows. A within-subjects study in which 24 participants completed complex geometry editing tasks in VR simulate scenarios where VR scenes are controlled by numerical parameters. Compared with the condition where disambiguation is not available, quantitative metrics and qualitative feedback indicate that a hybrid approach which combines clarification questions and graphical previews can support better interaction stability with fewer conversation rounds while improving user experience. These findings provide empirical evidence on the effectiveness of disambiguation methods in LLM-assisted editing of parameter-driven immersive scenes and inform design guidelines for future integration of LLMs in advanced VR/AR systems.

cs.HC↗

arXiv2026

A Taxonomy of Confabulations and the Perception-Reality Gap in LLM-Assisted Immersive Scene Editing

Large language models (LLMs) are being increasingly integrated into immersive environments and design workflows, providing application prospects in areas such as rapid scene prototyping for non-expert users and scene understanding capabilities for accessibility design. While many workflows that incorporate LLMs in immersive spaces are proposed, such systems can exhibit errors, potentially resulting in frustration, loss of user trust, and compromised user safety. This paper studies the underexplored area of LLM confabulations in immersive 3D scene editing contexts. Through an exploratory study with 24 non-expert users, we construct a taxonomy of the different types of confabulation observed in LLM-assisted immersive 3D scene editing. We report their prevalence and disruptiveness, and define the construct perception-reality gap to help understand the gap between the actual and perceived occurrence of confabulations. We highlight the observed saturation of confabulation awareness under load and conclude by discussing design implications for confabulation mitigation in future LLM-assisted systems in immersive 3D scenes.

cs.HC↗

arXiv2026

Firewalls to Secure Dynamic LLM Agentic Networks

The emergence of agent-to-agent communication protocols mirrors the early internet: powerful connectivity with minimal security infrastructure. When AI agents communicate on behalf of users, every message crosses a trust boundary where the user's personal data and the external agent's unconstrained language each present distinct risks. We address both through a dual-firewall architecture grounded in a unifying principle: each task defines a context, and both sides of the communication carry information far exceeding what that context requires. Our firewalls act as projections onto the task context, allowing only contextually appropriate content to cross each boundary. The Language Converter Firewall projects incoming messages onto a closed, domain-specific, structured protocol; an external agent's message is converted to validated fields while persuasive framing, urgency tactics, and embedded instructions are structurally eliminated through deterministic verification. This replaces the asymmetric challenge of resisting every possible manipulation with the structural guarantee that manipulation has no channel through which to arrive. The Data Abstraction Firewall projects outgoing information onto the granularity appropriate for the task, rather than applying binary disclose-or-redact filtering, as previous airgapping solutions did. Both firewalls operate in a trusted environment isolated from external input, applying domain-specific rules learned automatically from demonstrations. Across 864 attacks spanning three domains on the ConVerse benchmark, our architecture reduces privacy attack success rates (e.g., from 84% to 10% for GPT-5) and security attacks (from 60% to 3%), while maintaining or even improving task completion quality.

cs.CR↗

arXiv2026

MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning

Reinforcement learning (RL)-based biomechanical simulations have the potential to revolutionise HCI research and interaction design, but currently lack usability and interpretability. Using the Human Action Cycle as a design lens, we identify key limitations of biomechanical RL frameworks and develop MyoInteract, a novel framework for fast prototyping of biomechanical HCI tasks. MyoInteract allows designers to setup tasks, user models, and training parameters from an easy-to-use GUI within minutes. It trains and evaluates muscle-actuated simulated users within minutes, reducing training times by up to 98%. A workshop study with 12 interaction designers revealed that MyoInteract allowed novices in biomechanical RL to successfully setup, train, and assess goal-directed user movements within a single session. By transforming biomechanical RL from a days-long expert task into an accessible hour-long workflow, this work significantly lowers barriers to entry and accelerates iteration cycles in HCI biomechanics research.

cs.HC↗

arXiv2026

Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends

With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, benefiting various downstream domains such as game engines, embodied AI, autonomous driving, etc. Through explicitly incorporating user actions into world state transition, recent literature empowers world modeling with interactivity in an action-conditioned video or 3D generation paradigm, further enhancing controllability over world evolutions and facilitating users to freely traverse, manipulate, navigate, and personalize the state evolution. In this paper, we aim to systematically review recent research trends, technical developments, evaluation benchmarks, and also propose future potential directions in interactive world modeling. Specifically, we first summarize recent efforts and trends in terms of application scenarios, world state evolution, and scene modality. Afterwards, we delve into three crucial technical challenges, including action-conditioned controllability, long-horizon interactions and memory, and action-following responsiveness for real-time interactivity. Furthermore, we also thoroughly compare existing benchmarks and metrics in four specific application fields: open-world exploration, game engine, autonomous driving, and robotics. Finally, we discuss several promising future directions in achieving next-generation interactive world modeling. The corresponding repository is publicly available at: https://github.com/liujiuming123/Awesome-Interactive-World-Model.

cs.CV↗

arXiv2026

Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes

As more applications of large language models (LLMs) for 3D content for immersive environments emerge, it is crucial to study user behaviour to identify interaction patterns and potential barriers to guide the future design of immersive content creation and editing systems which involve LLMs. In an empirical user study with 12 participants, we combine quantitative usage data with post-experience questionnaire feedback to reveal common interaction patterns and key barriers in LLM-assisted 3D scene editing systems. We identify opportunities for improving natural language interfaces in 3D design tools and propose design recommendations for future LLM-integrated 3D content creation systems. Through an empirical study, we demonstrate that LLM-assisted interactive systems can be used productively in immersive environments.

cs.HC↗

arXiv2026

Unbounded: Object-Boundary Interaction in Mixed Reality

Boundaries such as walls, windows, and doors are ubiquitous in the physical world, yet their potential in mixed reality (MR) remains underexplored. We present Unbounded, a Research through Design inquiry into object--boundary interaction (OBI). Building on prior work, we articulate a design space aimed at providing a shared language for OBI. To demonstrate its potential, we design and implement eight examples across productivity and art exploration scenarios, showcasing how OBIs can enrich and reframe everyday interactions. We further engage with six MR experts in one-on-one feedback sessions, using the design space and examples as design probes. Their reflections broaden the conceptual scope of OBI, reveal new possibilities for how the framework may be applied, and highlight implications for future MR interaction design. https://www.zhuoyuelyu.com/unbounded

cs.HC↗

arXiv2026

Objestures: Everyday Objects Meet Mid-Air Gestures for Expressive Interaction

Everyday object-based interactions (EOIs) and mid-air gesture interactions (MAIs) have been widely explored, yet prior work on their integration often targets narrow use cases or specific technologies, leaving designers and developers with limited guidance that generalizes across diverse EOIs and MAIs. We introduce Objestures ("Obj" + "Gestures") -- five interaction types spanning EOIs and MAIs, forming a design space for expressive uni- and bimanual interaction. To evaluate the usefulness of Objestures, we conducted an exploratory user study (N=12) on basic 3D tasks (rotation and scaling), which showed performance comparable to the headset's native freehand manipulation. To understand the user experience, we conducted case studies with the same participants across three applications (Sound, Draw, and Shadow), where participants found the interactions intuitive, engaging, and expressive, and indicated interest in everyday use. We further demonstrate the potential of Objestures across diverse contexts through 30 examples, and discuss limitations and implications. https://www.zhuoyuelyu.com/objestures

cs.HC↗

arXiv2026

Grand Challenges around Designing Computers' Control Over Our Bodies

Advances in emerging technologies, such as on-body mechanical actuators and electrical muscle stimulation, have allowed computers to take control over our bodies. This presents opportunities as well as challenges, raising fundamental questions about agency and the role of our bodies when interacting with technology. To advance this research field as a whole, we brought together expert perspectives in a week-long seminar to articulate the grand challenges that should be tackled when it comes to the design of computers' control over our bodies. These grand challenges span technical, design, user, and ethical aspects. By articulating these grand challenges, we aim to begin initiating a research agenda that positions bodily control not only as a technical feature but as a central, experiential, and ethical concern for future human-computer interaction endeavors.

cs.HC↗

arXiv2026

How Do We Evaluate Experiences in Immersive Environments?

How do we evaluate experiences in immersive environments? Despite decades of research in immersive technologies such as virtual reality, the field remains fragmented. Studies rely on overlapping constructs, heterogeneous instruments, and little agreement on what counts as immersive experience. To better understand this landscape, we conducted a bottom-up scoping review of 375 papers published in ACM CHI, UIST, VRST, SUI, IEEE VR, ISMAR, and TVCG. Our analysis reveals that evaluation practices are often domain- and purpose-specific, shaped more by local choices than by shared standards. Yet this diversity also points to new directions. Instead of multiplying instruments, researchers benefit from integrating and refining them into smarter measures. Rather than focusing only on system outputs, evaluations must center the user's lived experience. Computational modeling offers opportunities to bridge signals across methods, but lasting progress requires open and sustainable evaluation practices that support comparability and reuse. Ultimately, our contribution is to map current practices and outline a forward-looking agenda for immersive experience research.

cs.HC↗

arXiv2026

Cost-Aware Bayesian Optimization for Prototyping Interactive Devices

Deciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries, can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only ${\approx}70\%$ of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.

cs.HC↗

arXiv2026

A Multi-Camera Optical Tag Neuronavigation and AR Augmentation Framework for Non-Invasive Brain Stimulation

Accurate neuronavigation is essential for generating the intended effect with transcranial magnetic stimulation (TMS). Precise coil placement also directly influences stimulation efficacy. Traditional neuronavigation systems often rely on costly and still hard to use and error-prone tracking systems. To solve these limitations, we present a computer-vision-based neuronavigation system for real-time tracking of patient and TMS instrumentation. The system can feed the necessary data for a digital twin to track TMS stimulation targets. We integrate a self-coordinating optical tracking system with multiple consumer-grade cameras and visible tags with a dynamic 3D brain model in Unity. This model updates in real time to represent the current stimulation coil position and the estimated stimulation point to intuitively visualize neural targets for clinicians. We incorporate an augmented reality (AR) module to bridge the gap between the visualization of the digital twin and the real world and project the brain model in real-time onto the head of a patient. AR headsets or mobile AR devices allow clinicians to interactively view and adjust the placement of the stimulation transducer intuitively instead of guidance through abstract numbers and 6D cross hairs on an external screen. The proposed technique provides improved spatial precision as well as accuracy. A case study with ten participants with a medical background also demonstrates that the system has high usability.

cs.HC↗

arXiv2026

Optical Tag-Based Neuronavigation and Augmentation System for Non-Invasive Brain Stimulation

Accurate neuronavigation is critical for effective transcranial magnetic stimulation (TMS), as stimulation outcomes depend directly on precise coil placement. Existing neuronavigation systems are often costly, complex, and prone to tracking errors. To address these limitations, we present a computer vision based neuronavigation system that enables real time tracking of the patient and TMS instrumentation. The system integrates a multi camera optical tracking setup with consumer grade hardware and visible markers to drive a digital twin of the stimulation process. A dynamic 3D brain model in Unity updates in real time to visualize coil position and estimated stimulation targets. Augmented reality (AR) is further incorporated to project this model directly onto the patient's head, enabling intuitive, in situ coil adjustment without reliance on abstract numerical displays. Overall, the proposed approach improves spatial precision and accuracy while enhancing usability.

cs.HC↗

arXiv2026

LocoScooter: Designing a Stationary Scooter-Based Locomotion System for Navigation in Virtual Reality

Virtual locomotion remains a challenge in VR, especially in space-limited environments where room-scale walking is impractical. We present LocoScooter, a low-cost, deployable locomotion interface combining foot-sliding on a compact treadmill with handlebar steering inspired by scooter riding. Built from commodity hardware, it supports embodied navigation through familiar, physically engaging movement. In a within-subject study (N = 14), LocoScooter significantly improved immersion, enjoyment, and bodily involvement over joystick navigation, while maintaining comparable efficiency and usability. Despite higher physical demand, users did not report increased fatigue, suggesting familiar movements can enrich VR navigation.

cs.HC↗

arXiv2025

ImageTalk: Designing a Multimodal AAC Text Generation System Driven by Image Recognition and Natural Language Generation

People living with Motor Neuron Disease (plwMND) frequently encounter speech and motor impairments that necessitate a reliance on augmentative and alternative communication (AAC) systems. This paper tackles the main challenge that traditional symbol-based AAC systems offer a limited vocabulary, while text entry solutions tend to exhibit low communication rates. To help plwMND articulate their needs about the system efficiently and effectively, we iteratively design and develop a novel multimodal text generation system called ImageTalk through a tailored proxy-user-based and an end-user-based design phase. The system demonstrates pronounced keystroke savings of 95.6%, coupled with consistent performance and high user satisfaction. We distill three design guidelines for AI-assisted text generation systems design and outline four user requirement levels tailored for AAC purposes, guiding future research in this field.

cs.HC↗