deploy passive haptics

This study addresses the limitations of gesture-based interaction in virtual reality (VR), where the absence of tactile feedback and physical reference surfaces leads to reduced precision and efficiency. To overcome these challenges, the authors propose a multimodal interaction approach that integrates a portable physical surface with vibrotactile/pressure haptic feedback and visual cues. The work presents the first systematic comparison among three conditions: no feedback, haptic-only feedback, and the inclusion of a physical surface. Experimental results demonstrate that the physical surface significantly improves selection accuracy, tracing efficiency, and drawing quality. Furthermore, it effectively facilitates bimanual coordination and enhances users’ sense of control and confidence during interaction, offering a practical and efficient solution for high-precision touch-based tasks in VR.

High-fidelity tactile feedback is crucial for enhancing immersion in virtual reality, yet existing methods struggle to efficiently generate realistic haptic textures. To address this challenge, this work proposes HapticMatch, a novel framework that leverages diffusion and flow-matching models to synthesize renderable microscale surface geometries directly from a single RGB image, enabling rapid “scan-to-touch” prototyping. The study introduces the first aligned multimodal material dataset, integrating microscale optical images, height maps, and friction-induced vibration signals. By combining conditional generative models with VR/AR interaction techniques, HapticMatch significantly lowers the barrier to haptic content creation and substantially improves visuo-tactile consistency in virtual environments.

Balancing exploratory creativity and technical reproducibility remains challenging in haptic design, particularly for everyday wearable contexts. Method: This paper introduces a dynamic pin-array haptic design toolkit tailored for daily wearables. It pioneers the “haptic crafting” metaphor—a framework integrating tactile translation through manual prototyping with real-time embodied motion capture—to establish a closed-loop design process from abstract ideation to embodied prototyping. The toolkit comprises a dynamic pin-array actuator, a custom gesture-tracking module, modular hardware interfaces, and a digital encoding scheme for haptic patterns. Contribution/Results: It enables full-body, unconstrained exploration and multi-designer collaborative semantic co-creation. Evaluated with 14 designers and artists, the toolkit significantly enhances creative divergence and haptic expressiveness, yielding dozens of reusable, iteratively refined embodied haptic vocabularies.

This study addresses the challenge of high-fidelity tactile rendering of 3D geometric shapes in virtual reality, a task hindered by limited spatiotemporal resolution in existing vibrotactile approaches. Inspired by the natural deformation of the fingertip during object contact, this work proposes a novel parametric tactile rendering method that integrates fingerpad deformation modeling into electro-tactile feedback design. Leveraging a finger-worn electro-tactile interface, the system dynamically generates spatiotemporal tactile patterns based on interaction states—such as proximity, contact, and sliding—and geometric context, including shape features and surface textures. A user study (N=24) demonstrates that the proposed approach significantly outperforms baseline systems in both texture discrimination and geometric feature recognition, offering a new paradigm and practical guidance for the development of high-fidelity tactile interfaces.

The field of tactile surface modeling and data representation has long lacked a systematic treatment from a signal processing perspective, resulting in technical fragmentation across modeling, acquisition, rendering, and perception. Method: This paper presents the first unified taxonomy, comparative analysis, and critical review of tactile surface modeling methods—organized along core signal processing dimensions: signal sampling, feature extraction, compression coding, geometry–physics hybrid modeling, and cross-modal representation. Contribution/Results: We construct an end-to-end technical roadmap, identifying six fundamental modeling paradigms and three critical representation bottlenecks: (1) resolution–bandwidth trade-offs, (2) insufficient physical fidelity, and (3) challenges in cross-modal alignment. Our framework provides a scalable theoretical foundation and concrete technical pathways toward standardized tactile modeling, real-time rendering, and closed-loop perception.

研究对比了手部追踪和控制器在VR互动任务中的触觉反馈效果，通过振动、脉冲及无反馈三种方式评估性能、工作负荷和偏好。

This work addresses the loss of rich contact details in sim-to-real transfer due to oversimplified tactile representations by proposing a physics-informed center-of-pressure (CoP) tactile representation. This approach preserves dense contact information while enhancing transfer robustness and enables sensor orientation calibration without requiring ground-truth force measurements through differentiable dynamics. Integrated with reinforcement learning and multi-fingered dexterous hand control, the system achieves zero-shot sim-to-real transfer on vision-deprived tasks such as plug insertion and ball balancing. It significantly outperforms binary contact and raw tactile baselines, with the learned policy implicitly encoding physical properties of objects, including mass.

This study addresses information overload and the lack of enhanced haptic feedback in touch-screen visualizations by integrating vibrotactile encoding into line chart tooltips to establish a multimodal interaction channel that reinforces trend perception. Through parameter preference studies and pairwise comparison experiments, optimal haptic feedback configurations were identified. Results demonstrate that this design significantly improves user engagement and interaction experience while maintaining data interpretation accuracy. These findings confirm that haptic feedback serves as an effective complement to the visual channel, providing a novel design paradigm and empirical evidence for multimodal data visualization.

In extended reality (XR) environments lacking physical haptic feedback, how users perceive the stiffness of virtual objects—such as C$_{60}$ molecules—remains unclear. This study addresses this gap by integrating an interactive molecular dynamics XR system (iMD-XR), psychophysical experiments, and systematic stiffness parameter modulation to quantify, for the first time, user perceptual thresholds for virtual molecular stiffness in the absence of tactile cues. Results demonstrate that the just-noticeable difference (JND) under direct interaction is 11.5%, significantly lower than the 18.5% observed under passive viewing alone. Moreover, engaging in interaction prior to observation markedly enhances perceptual accuracy during subsequent viewing. These findings reveal the facilitative role of active interaction in perceiving virtual object properties and demonstrate a cross-modal transfer effect from action to perception.

This work addresses the challenge of enhancing robotic performance in contact-intensive tasks without relying on physical tactile sensors. The authors propose TacImag, a framework that predicts tactile signals—such as force fields or tactile images—from visual and proprioceptive inputs, using these predictions as supervisory signals for policy learning. TacImag is the first approach to enable tactile-augmented manipulation control without deploying actual tactile hardware, demonstrating that the core benefit of tactile imagination lies not in faithfully reconstructing tactile data but in providing contact-aware representations that facilitate policy optimization. Experiments across six simulated and four real-world tasks show substantial performance gains: force-field representations improve contact-sensitive tasks by 44.4% on average, while tactile-image representations boost texture-sensitive tasks by 23.3%.