AI & ML
Metamaterial tactile displays with programmable virtual pixels
arXiv cs.HC0 views
arXiv:2606.05572v2 Announce Type: replace-cross Abstract: Tactile displays present information by supplying localized mechanical signals across their surface, but conventional approaches often require an actuator to be positioned at every location where feedback is provided, limiting resolution and increasing complexity. Wave-mediated tactile displays exploit wave transmission to generate virtual pixels using far fewer actuators, but are constrained in resolution by the intrinsic dispersion characteristics of the display medium. Here, we show how this constraint can be overcome by applying metamaterial physics to engineer wave transmission in a rigid plate using an optimized lattice of resonators. The resulting system exhibits a slow-wave dispersion branch that reduces the wavelengths available for constructing virtual pixels, thereby enhancing display resolution by an order of magnitude. We demonstrate a metamaterial tactile display driven by eight peripheral actuators that is capable of generating localized virtual tactile pixels at any surface location with centimeter-scale resolution. The location, timing, and waveform of each pixel can be specified computationally, and the output bandwidth, 75-400 Hz, spans a substantial portion of the tactile frequency range. In four perceptual experiments, participants localized, explored, and discriminated multi-pixel patterns comprising transient and sustained vibrations with accuracies ranging from 95.3% to 100%. This research provides metamaterial engineering methods that enable the design of tactile displays driven by few actuated degrees of freedom, with implications for many future application areas.
Source:https://arxiv.org/abs/2606.05572PreviousMAIA: Multi-Agent Intent Articulation for Requirement Discovery in Art CommissionsNextUsing profiles of cognitive capability to assess AI suitability for workplace tasks
Related posts
A Bayesian Optimized Biquad Filter-Burg Model for Real-Time EEG Phase Estimation
0arXiv eess.SP
Thought-deciphering via hierarchical EEG-to-text alignment
0arXiv eess.SP
Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells
0arXiv eess.SP
Benchmarking Sensor-Fault Robustness in Forecasting
0arXiv eess.SP
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
0arXiv eess.SP
Multi-Threshold Sampling: Signal Space, Sampling Operators, and First Recorded Crossing-Time Distributions
0arXiv eess.SP