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Unsupervised learning of haptic material properties.

Metzger, A. and Toscani, M., 2022. Unsupervised learning of haptic material properties. eLife, 11, e64876.

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metzger_toscani_2022.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.7554/eLife.64876


When touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here, we show that perceptual haptic representation of materials emerges from efficient encoding of vibratory patterns elicited by the interaction with materials. We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed representation (i.e., latent space) allows for classification of material categories (i.e., plastic, stone, wood, fabric, leather/wool, paper, and metal). More importantly, classification performance is higher with perceptual category labels as compared to ground truth ones, and distances between categories in the latent space resemble perceptual distances, suggesting a similar coding. Crucially, the classification performance and the similarity between the perceptual and the latent space decrease with decreasing compression level. We could further show that the temporal tuning of the emergent latent dimensions is similar to properties of human tactile receptors.

Item Type:Article
Uncontrolled Keywords:efficient coding ; haptic perception ; human ; materials ; natural textures ; neuroscience ; touch ; unsupervised deep learning
Group:Faculty of Science & Technology
ID Code:36687
Deposited By: Symplectic RT2
Deposited On:01 Mar 2022 11:35
Last Modified:14 Mar 2022 14:33


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