Touch Modality Identification With Tensorial Tactile Signals: A Kernel-Based Approach

Abstract

Touch modality identification has attracted increasing attention due to its importance in human&#x2013;robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods. <i>Note to Practitioners</i>&#x2014;In a wealth of tactile recognition tasks, we are in the face of various challenges. For instance, tactile measurements are commonly tensorial and high-dimensional. The misalignments among tactile measurements prevail, such as different durations of tactile measurements and the misaligned starting time point of tactile measurements. This article presents a kernel-based method using an ideal regularized composite kernel to deal with all challenges in a unified framework. The kernel-based method consists of two key components including the SVD-based kernel and the global alignment kernel. The proposed method may shed new insights on new advances in tactile signal processing particularly in human&#x2013;robot interactions.