Exploring Feature-Based Learning for Data-Driven Haptic Rendering

In this work we extend ideas of machine learning to the domain of data-driven haptic rendering. The proposed approach facilitates the processing of high-dimensional haptic interaction signals, which so far proved too difficult for existing data-driven methods. The key idea is to construct a compact feature space in the frequency domain which allows for efficient data reduction via a feature selection process. First, in a recording stage, extensive force and displacement datasets are acquired in automated measurements on deformable sample objects. These data are then transformed into a dimensionally reduced, compact frequency space representation. Next, feature-based learning is carried out in this feature space to significantly reduce the size of the original dataset. Based on this, time-domain haptic models capable of real-time performance are finally generated to encode the forces arising from bimanual object interactions. The presented processing chain is generally applicable and extendable to more complex interactions with even higher-dimensional data. The resulting haptic models are directly usable for data-driven haptic rendering. We illustrate the improved performance in comparison with previously existing data-processing approaches.