Head-rotation gains can reduce the physical effort required to look around in seated virtual reality (VR), but their applicability is constrained by perceptual detectability and discomfort. This study investigates whether neck muscle vibration (NMV) can alter the perception of head-rotation gains during seated VR. In a within-subject study with 20 participants, we compared four vibration conditions and measured detection thresholds, subjective discomfort, and perceived controllability. We found that stimulating the sternocleidomastoid muscle contralateral to the turning direction shifted the point of subjective equality (PSE) toward larger gains. This proprioceptive bias indicates that a wider range of amplified head rotation remained imperceptible, and importantly, this condition also reduced discomfort relative to other NMV conditions. These findings suggest that modulating multisensory integration via direction-dependent NMV is a promising haptic approach for seated VR techniques that benefit from modest rotation-gain amplification.
In the field of robotic grasping, robots typically employ uniform grasping forces for object manipulation. However, when dealing with soft objects like plastic cups, this consistent force may cause deformation due to their compliant nature. Therefore, adapting grasping forces to different object properties is crucial for maintaining object integrity. With the rapid advancement of vision-language models (VLMs), the ability to deeply understand the characteristics of grasped objects has been significantly enhanced. Building upon this, this paper proposes an Anthropomorphic Physical Property Comprehension Grasping (APPCG) framework that integrates visual physical-property priors with tactile verification. Our approach begins with VLM-based inference of target object properties to generate structured physical descriptors. Initial grasping forces are then computed using mechanical equilibrium equations. During preliminary grasping execution, GelSight sensors capture contact deformation to estimate real-time contact forces via Hooke's law and detecting deformation through spatiotemporal criteria ($r_{d}^{t}$, $MSE^{t}$). Finally, distinguishing rigid or flexible objects by stiffness, we dynamically determine final grasping forces, enabling robotic adaptation to object-specific characteristics. Experiments across daily objects demonstrate the framework's effectiveness: rigid objects achieved 97.7% success (127/130) while flexible objects attained 98.5% success (197/200).
Natural surfaces contain local spatial irregularities, yet tactile texture perception has often been characterized in terms of mean geometric properties. It remains unclear whether information associated with such irregularities improves roughness-matching precision when the mean spatial scale of a texture is varied. We compared roughness-matching precision between physically fabricated periodic and random gratings with nominal mean wavelengths of 1.0 and 2.0 mm. Participants selected, from a set of test gratings, the stimulus whose roughness they perceived as closest to that of a reference. Matching precision was quantified for each participant and condition using the standard deviation of the nominal wavelengths selected across repeated trials, with a smaller standard deviation indicating more precise roughness matching. At the nominal mean wavelength of 1.0 mm, the standard deviation was significantly smaller for the random gratings than for the periodic gratings. No significant difference was observed at 2.0 mm. These findings suggest that information contained in the 1.0-mm random gratings beyond their mean wavelength may have contributed to roughness matching precision.
This paper introduces novel visuo-haptic coupling techniques dedicated to the rendering of contact with objects in virtual reality (VR) using ultrasound mid-air haptics (UMH). More specifically, our objective is to propose various visual and haptic feedback mechanisms for enhancing user perception of interpenetration during interactions with virtual objects. We conducted user studies to evaluate combinations of four visual feedback techniques and three UMH-based haptic techniques, comparing their effects on user experience and contact accuracy. Through the studies, we identified that feedback schemes combining ghost hand visual cues with shrinking circle haptics yield the most intuitive and precise contact perception, while more subtle cues were subjectively preferred. These findings offer insights for advancing user experience and interaction quality in immersive VR environments.
In this study we compare navigation performance and user preferences with three methods of vibrotactile feedback on the torso: Guidance, which informed the user where to go; Surround, which informed the user of nearby obstacles; and Hand-held, which informed the user of obstacles that they pointed at with a hand-held scanner. We aim to provide direct comparisons between these commonly used feedback methods, as varying set-ups in prior literature make it difficult to compare results. 20 participants walked through three virtual environments (Maze, Hallway with turns, and room with Pillars) with invisible obstacles towards a visible destination marker and back to the start. The best navigation performance was achieved with Guidance feedback (median mean trial durations: 31 s for Guidance, 38 s for Surround, and 46 s for Hand-held). Participants also rated Guidance highest on usability. The difference between Guidance and Surround was relatively small in the Hallway and Pillar environments. Collision rates were not significantly different between when Guidance or Surround feedback was used (trials with collisions: 19% for Guidance, 23% for Surround, 36% for Hand-held). Hand-held feedback scored worst across all metrics. Participants were quicker when returning from the destination compared to walking towards the destination with both obstacle detection feedback methods (median return-toward duration ratios: 0.96 for Guidance, 0.75 for Surround, 0.73 for Hand-held). When efficient navigation has priority, Guidance feedback appears to be the best option. In situations where guidance along a path is not possible or desirable, Surround feedback is a better option than Handheld.
