A haptic texture database for tool-mediated texture recognition and classification

Abstract

Abstract-While stroking a rigid tool over an object surface, vibrations induced on the tool, which represent the interaction between the tool and the surface texture, can be measured by means of an accelerometer. Such acceleration signals can be used to recognize or to classify object surface textures. The temporal and spectral properties of the acquired signals, however, heavily depend on different parameters like the applied force on the surface or the lateral velocity during the exploration. Robust features that are invariant against such scan-time parameters are currently lacking, but would enable texture classification and recognition using uncontrolled human exploratory movements. In this paper, we introduce a haptic texture database which allows for a systematic analysis of feature candidates. The publicly available database includes recorded accelerations measured during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures. As a preliminary feature analysis, we test and compare six wellestablished features from audio and speech recognition together with a Gaussian Mixture Model-based classifier on our recorded free hand signals. Among the tested features, best results are achieved using Mel-Frequency Cepstral Coefficients (MFCCs), leading to a texture recognition accuracy of 80.2%.

Recent progress on tactile object recognition

“…Representative textured objects used for texture-based tactile recognition. Left: the 20 material surfaces used in the study by Sinapov et al 15 ; middle: the 8 material surfaces used in the study by Jamali and Sammut 32 ; right: the 43 material surfaces used in the study by Strese et al 34 Those figures have been reproduced with permission from IEEE. Figure 4.…”

“…Common methods for classification include the nearest-neighbor classifier, support vector machine (SVM), 15,35 and Gaussian process. 34 One example of using the SVM classifier is seen in the study by Chathuranga et al 36 In this study, magnetic flux obtained three-dimension tactile values and treated the covariance matrix of these values as features to classify eight texture materials by SVM. More recently, Baishya and Bauml 37 developed deep learning method for robust material classification with tactile skins.…”

“…Right: hardware setup used for the texture recording in the study by Strese et al 34 Those figures have been reproduced with permission from IEEE. 15,32,34 other hand, in practical applications, complex signals imply some potential structure information in addition to sparsity. The question of how to make full use of this information has increased the academic attention on structured sparse coding.…”

“…All of these works extracted features using the discrete Fourier transform or other frequency domain analysis tools. From another viewpoint, Strese et al 34 pointed out the similarity between tactile and speech signals, and resorted to the Mel-frequency cepstral coefficients, which are commonly used in audio processing to extract features. Common methods for classification include the nearest-neighbor classifier, support vector machine (SVM), 15,35 and Gaussian process.…”

“…In the study by Strese et al, 34 the authors introduced a haptic texture database which includes acceleration measurements collected during controlled and well-defined texture scans, as well as uncontrolled human free hand texture explorations for 43 different textures.…”