Unsupervised learning of haptic material properties
- Department of Psychology, Bournemouth University, United Kingdom
- Department of Psychology, Justus-Liebig University, Germany
Figures
Figure 1
Materials and vibratory signals.
(A) Examples of materials. One example per category is shown. (B) Accelerometer mounted on a 3D-printed pen with a steel tip used for acquisition of vibratory signals. (C) Original and reconstructed …
Figure 2
Perceptual ratings.
(A) Perceptual representation within the first (x-axis) and the second (y-axis) principal components space. Principal components analysis (PCA) was performed on the z-transformed ratings pooled over …
Figure 3
Latent space representation of the Autoencoder.
(A) Centers of categories in the t-distributed stochastic neighbor embedding (t-SNE). Each color represents a different category, as indicated in the legend. Dots represent material categories …
Figure 4
Similarity between the perceptual and the latent representation of the Autoencoder.
(A) Distance matrix based on the distances between the category centers in the (10D) latent PCs space. (B) Relationship between perceptual distances (y-axis) and distances between categories in the …
Figure 5
Frequency tuning.
(A) Comparison between temporal tuning of the dimensions of the 10D latent PCs space and the temporal tuning of the Pacinian (PC) and rapidly adapting (RA) afferents (green and red dashed lines, …
