Generating haptic texture models from unconstrained tool-surface interactions

If you pick up a tool and drag its tip across a table, a rock, or a swatch of fabric, you are able to feel variations in the textures even though you are not directly touching them. These vibrations are characteristic of the material and the motions made when interacting with the surface. This paper presents a new method for creating haptic texture models from data recorded during natural and unconstrained motions using a new haptic recording device. The recorded vibration data is parsed into short segments that represent the feel of the surface at the associated tool force and speed. We create a low-order auto-regressive (AR) model for each data segment and construct a Delaunay triangulation of models in force-speed space for each surface. During texture rendering, we stably interpolate between these models using barycentric coordinates and drive the interpolated model with white noise to output synthetic vibrations. Our methods were validated through application to data recorded by eight human subjects and the experimenter interacting with six textures. We present a new spectral metric for determining perceptual match of the models in order to evaluate the effectiveness and consistency of the segmenting and modeling approach. Multidimensional scaling (MDS) on the pairwise differences in the synthesized vibrations shows that the 54 created texture models cluster by texture in a two-dimensional perceptual space.