An Inverse Neural Network Model for Data-Driven Texture Rendering on Electrovibration Display

With the introduction of variable friction displays, new possibilities have emerged in haptic texture rendering on flat surfaces. In this work, we propose a data-driven method for realistic texture rendering on an electrovibration display. We first describe a motorized linear tribometer we developed to collect lateral frictional forces from the textured surfaces under various scanning velocities and normal forces. We then propose an inverse dynamics model of the display to describe its output input relationship using nonlinear autoregressive with external input (NARX) neural networks. Forces resulting from applying a pseudo-random binary signal (PRBS) to the display are used to train each network under the given experimental condition. A comparison between the real and virtual forces in frequency domain shows promising results for recreating virtual textures similar to the real ones and also reveals the capabilities and limitations of the proposed method.