Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback

arXiv (Cornell University) · 2026-04-22

Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user-specific preference spaces over vibrotactile parameters via Gaussian-process-based uncertainty-aware preference learning. VPL uses an expected information gain-based acquisition strategy to guide query selection over 40 rounds of pairwise comparisons of overall user preference, augmented with user-reported uncertainty, enabling efficient exploration of the parameter space. We evaluate VPL in a user study (N = 13) using the vibrotactile feedback from a Microsoft Xbox controller, showing that it efficiently learns individualized preferences while maintaining comfortable, low-workload user interactions. These results highlight the potential of VPL for scalable personalization of vibrotactile experiences.

Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback

arXiv (Cornell University) · 2026-04-22

Individual differences in vibrotactile perception underscore the growing importance of personalization as haptic feedback becomes more prevalent in interactive systems. We propose Vibrotactile Preference Learning (VPL), a system that captures user-specific preference spaces over vibrotactile parameters via Gaussian-process-based uncertainty-aware preference learning. VPL uses an expected information gain-based acquisition strategy to guide query selection over 40 rounds of pairwise comparisons of overall user preference, augmented with user-reported uncertainty, enabling efficient exploration of the parameter space. We evaluate VPL in a user study (N = 13) using the vibrotactile feedback from a Microsoft Xbox controller, showing that it efficiently learns individualized preferences while maintaining comfortable, low-workload user interactions. These results highlight the potential of VPL for scalable personalization of vibrotactile experiences.

Exploring Remote Affective Communication Through a Haptic Wearable and Socially Assistive Robot

2026-03-07

Both haptic signals and simple, non-anthropomorphic robots can convey complex emotions and enhance remote communication. In this study, we integrated a zoomorphic socially expressive Blossom robot and a haptic sleeve to create a novel multimodal telepresence platform for remote social interaction. Through a within-subject user study with 16 participants, we explored the individual and combined effects of socially expressive robots and mediated social touch on affective communication and social presence during a semi-collaborative LEGO assembly task. Across all participants, the robot and wearable device significantly impacted how participants perceived expressions of gratitude, calming, attention-grabbing, and sadness, evaluated through self-reported valence and arousal. The robot and wearable device in our setting did not show a significant effect on social presence. The observations from this exploratory study can inform the design of multimodal telepresence systems and interactions using non-anthropomorphic robots and mediated touch.

AI for Haptics and Haptics for AI: Challenges and Opportunities

2026-04-13 · 1 citations

AI has transformed methods and knowledge across many domains. However, the intersection of AI and haptics remains underexplored. While modern AI techniques – fueled by machine learning and using powerful techniques such as generative modeling and reinforcement learning – offer powerful opportunities for advancing haptic design, insights from haptics research, such as perception modeling and adaptive interaction - grounded in human touch, embodiment, and multisensory integration — can also play a critical role in shaping more human-centered AI systems. This workshop will bring together an interdisciplinary community of researchers from HCI, haptics, AI, robotics, and design to (1) identify pressing questions in haptics that could benefit from AI approaches and (2) highlight ways in which haptic knowledge can support the development of embodied and context-aware AI. Through position papers and paper presentations, we will map key challenges, exchange methods, and explore new research directions that connect the two fields. By framing haptics and AI as mutually reinforcing, the workshop aims to build a shared research agenda and foster collaborations that advance both the science of touch and the design of intelligent interactive systems.

Crocheted Capacitive Touch Sensors for Rapid Prototyping of Soft Interfaces

2026-03-29

This work presents a novel approach to fabricating soft capacitive tactile sensors using a surface crochet technique to embed conductive thread within crocheted textile substrates. The sensors are mechanically compliant, low-cost, removable, and can be incorporated into a wide range of semi-open-mesh textile substrates, including crocheted, knitted, and loosely woven fabrics. To examine the influence of textile structure and fiber material on sensing performance, we fabricated sensors from acrylic, bamboo, and faux fur yarns, and evaluated their binary touch detection accuracy across four force levels and their signal-to-noise ratio over 30 trials per material. A user study with 15 participants revealed that integrating the sensors significantly affected the perceived tactile qualities of each textile substrate. Finally, we evaluated the sensors in a potential real-world use case: enabling touch-based interactions with a soft, zoomorphic socially assistive robot. Quantitative and qualitative findings highlight trade-offs between sensor performance, perceived tactile qualities, and affective impressions of the robot, informing design considerations for integrating textile-based tactile sensing in soft robotic systems.

Language-Guided Multimodal Texture Authoring via Generative Models

2026-03-29

SWIMVR: Simulating Water Interaction Using Multimodal Vibro-Thermal Rendering

2026-03-29

Reproducing realistic fluid interactions in Virtual Reality (VR) remains an open challenge due to their dynamic and multimodal nature. In this paper, we introduce SWIMVR, a wearable vibro-thermal haptic glove that simulates handwater interactions in VR through coordinated cold thermal and vibrotactile feedback. SWIMVR delivers event-driven, spatiotemporally modulated actuation to render both surface impact and submerged flow sensations. Built around 14 micro-thermoelectric cooling modules and 13 vibrotactile actuators per hand, the system operates in real-time with a VR ocean environment rendered in Unity. We conducted two user studies optimizing thermal waveform patterns for perceptual stability and comfort, and actuator layouts for perceptual fidelity with minimal hardware. Results show that triangular thermal modulation, achieved by cycling between two cooling levels rather than maintaining a fixed output, yields the most perceptually stable cold sensations, while a reduced actuator configuration closely matches the full hand in spatial coverage and subjective realism. A follow-up study con-firmed that palmar-only actuation can evoke cold sensations on the dorsal side, allowing further hardware reductions. SWIMVR demonstrates that timing and spatial targeting of multimodal cues can produce strong subjective impressions of water interaction in VR, offering a pathway towards scalable, untethered haptic feedback for immersive environments.

Language-Guided Multimodal Texture Authoring via Generative Models

arXiv (Cornell University) · 2026-04-07

Authoring realistic haptic textures typically requires low-level parameter tuning and repeated trial-and-error, limiting speed, transparency, and creative reach. We present a language-driven authoring system that turns natural-language prompts into multimodal textures: two coordinated haptic channels - sliding vibrations via force/speed-conditioned autoregressive (AR) models and tapping transients - and a text-prompted visual preview from a diffusion model. A shared, language-aligned latent links modalities so a single prompt yields semantically consistent haptic and visual signals; designers can write goals (e.g., "gritty but cushioned surface," "smooth and hard metal surface") and immediately see and feel the result through a 3D haptic device. To verify that the learned latent encodes perceptually meaningful structure, we conduct an anchor-referenced, attribute-wise evaluation for roughness, slipperiness, and hardness. Participant ratings are projected to the interpretable line between two real-material references, revealing consistent trends - asperity effects in roughness, compliance in hardness, and surface-film influence in slipperiness. A human-subject study further indicates coherent cross-modal experience and low effort for prompt-based iteration. The results show that language can serve as a practical control modality for texture authoring: prompts reliably steer material semantics across haptic and visual channels, enabling a prompt-first, designer-oriented workflow that replaces manual parameter tuning with interpretable, text-guided refinement.

The Role of User Preferences in Fidgeting Devices for Supporting Attention

2026-03-29

This study examines how users' tactile preferences in fidgeting devices relate to attention during task performance. We developed a custom fidget cube with six keyboard-style switches (two clicky, two tactile, two linear) that varied in resistance and “clickiness”. In Phase 1, participants completed a Stroop test without fidgeting and while fidgeting with a mixed-button cube. Interaction logs from the mixed-button cube identified each participant's most and least preferred button types. In Phase 2, participants repeated the Stroop test while fidgeting with two single-button cubes-one containing only their most preferred button and one containing only their least preferred button. Participants were significantly less accurate when fidgeting with the mixed-button cube than when not fidgeting or when fidgeting with the single-button versions. They also showed faster reaction times when fidgeting with their preferred single-button cube compared to the mixed-button cube. Overall, the primary effect was a consistency benefit: restricting interaction to a single tactile signature improved accuracy, reaction time, and error rates relative to the mixed-button condition. These findings underscore the value of designing fidget and haptic feedback systems that minimize competing tactile cues in attention-critical contexts, and they suggest practical directions for therapeutic, educational, and workplace applications.