Stimuli Magnitude Adaptive Sample Selection for Data Driven Haptic Modeling
Data driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input output matching model identified by data sensed from interaction with target physical objects. In data driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application dependent threshold. This output and standard deviation based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning tree (BSP tree) and k means algorithms. We apply the new approach to data driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state of the art sample selection algorithms for regression tasks. Four kinds of haptic related behavior force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output approximation quality and computational complexity.
Entropy