Data-driven Texture Modeling and Rendering on Electrovibration Display

We propose a data-driven method for realistic texture rendering on an electrovibration display. To compensate the nonlinear dynamics of an electrovibration display, we use nonlinear autoregressive with external input (NARX) neural networks as an inverse dynamics model of an electrovibration display. The neural networks are trained with lateral forces resulting from actuating the display with a pseudo-random binary signal (PRBS). The lateral forces collected from the textured surface with various scanning velocities and normal forces are fed into the neural network to generate the actuation signal for the display. For arbitrary scanning velocity and normal force, we apply the two-step interpolation scheme between the closest neighbors in the velocity-force grid.