Haptic Recognition of Texture Surfaces Using Semi-Supervised Feature Learning Based on Sparse Representation

Haptic cognitive models are used to map the physical stimuli of texture surfaces to subjective haptic cognition, providing robotic systems with intelligent haptic cognition to perform dexterous manipulations in a manner that is similar to that of humans. Nevertheless, there is still the question of how to extract features that are stable and reflect the biological perceptual characteristics as the inputs of the models. To address this issue, a semi-supervised sparse representation method is developed to predict subjective haptic cognitive intensity in different haptic perceptual dimensions of texture surfaces. We conduct standardized interaction and perception experiments on textures that are part of common objects in daily life. Effective data cues sifting, perceptual filtering, and semi-supervised feature extraction steps are conducted in the process of sparse representation to ensure that the source data and features are complete and effective. The results indicate that the haptic cognitive model using the proposed method performs well in fitting and predicting perceptual intensity in the perceptual dimensions of “hardness,” “roughness,” and “slipperiness” for texture surfaces. Compared with previous methods, such as models using multilayer regression and hand-crafted features, the use of standardized interaction, cue sifting, perceptual filtering, and semi-supervised feature extraction could greatly improve the accuracy by improving the completeness of collected data, the effectiveness of features, and simulations of some physiological cognitive mechanisms. The improved method can be implemented to improve the performance of the haptic cognitive model for texture surfaces, and can also inspire research on intelligent cognition and haptic rendering systems.