pith. sign in

arxiv: 2605.29572 · v1 · pith:4AFTODV4new · submitted 2026-05-28 · 💻 cs.RO · cs.HC

Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models

Li Zou , Yasemin Vardar This is my paper

Pith reviewed 2026-06-29 06:57 UTC · model grok-4.3

classification 💻 cs.RO cs.HC
keywords tactile perceptionmaterial classificationmultisensory datainterpretable modelspsychophysical attributesthermal cuesrobotic hapticscompliance cues
0
0 comments X

The pith

Multisensory tactile data from pressing, static contact, and sliding, especially thermal cues, improves models of human material perception and classification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops an interpretable framework with three models to connect low-level tactile signals to human perceptual attributes and material recognition. Model 1 links interaction features to sensory attributes, Model 2 classifies materials from those attributes, and Model 3 does direct classification from features. Results indicate that using multiple interaction types boosts accuracy and that thermal information is key for both perception modeling and classification. This matters for creating robots and haptic devices that better mimic human touch by including often-missing thermal and compliance cues.

Core claim

Combining information from pressing, static contact, and sliding interactions improves prediction accuracy, and thermal cues are particularly informative for both perceptual modeling and material classification. These findings highlight the importance of thermal and compliance cues, which remain underrepresented in current robotic fingers and haptic displays. Incorporating such cues may enhance artificial systems' ability to approximate human material perception and guide the design of more perceptually grounded haptic interfaces.

What carries the argument

Three interconnected models: one mapping finger-surface interaction features to psychophysical sensory attributes, one classifying materials from those attributes, and one classifying directly from tactile features.

If this is right

  • Combining information from pressing, static contact, and sliding interactions improves prediction accuracy.
  • Thermal cues are particularly informative for both perceptual modeling and material classification.
  • Thermal and compliance cues remain underrepresented in current robotic fingers and haptic displays.
  • Incorporating thermal and compliance cues may enhance artificial systems' ability to approximate human material perception.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Sensor designers could prioritize adding thermal sensing to robotic fingers to close the gap with human perception.
  • The framework might extend to predicting perception of novel or composite materials not in the original dataset.
  • Haptic interfaces in virtual reality could simulate temperature changes to improve realism based on these perceptual links.

Load-bearing premise

The chosen tactile features and psychophysical sensory attributes sufficiently capture the relationship between low-level signals and human perceptual representations without significant loss or bias from sensor limitations or participant variability.

What would settle it

A controlled test on new participants where a model excluding thermal data matches the accuracy of the full multisensory model on material classification would falsify the claim that thermal cues are particularly informative.

Figures

Figures reproduced from arXiv: 2605.29572 by Li Zou, Yasemin Vardar.

Figure 1
Figure 1. Figure 1: Conceptual schematic of the model framework and data preparation. A. Human material recognition and modeling approach. Humans perceive multisensory tactile signals that convey surface attributes, which are integrated to recognize materials. Our framework approximates this process using three models: Model 1 maps extracted features from tactile signals to psychophysical ratings; Model 2 translates these per… view at source ↗
Figure 2
Figure 2. Figure 2: Results for interpreting the initial stage of human tactile processing, in which features are mapped to perceptual attributes (Model 1). (A) The performance of the regression models, evaluated using Mean Squared Error (MSE) and R-squared (R 2 ) scores. A lower MSE and a higher R 2 indicate better regression performance. All results were obtained using Random Forest regression, except for the null model, wh… view at source ↗
Figure 3
Figure 3. Figure 3: Results comparing models of higher-level human cognitive processing (Model 2) and direct AI-based classification (Model 3). (A) Classification accuracy for material identification using sensory attributes data, pressing data, static contact, sliding, and a combined dataset (pressing, static contact, and sliding). (B) Scatter plot of all materials in two-dimensional perceptual space derived from Principle C… view at source ↗
Figure 1
Figure 1. Figure 1: Conceptual schematic of the model framework and data preparation. A. Human material recognition and modeling approach. Humans perceive multisensory tactile signals that convey surface attributes, which are integrated to recognize materials. Our framework approximates this process using three models: Model 1 maps extracted features from tactile signals to psychophysical ratings; Model 2 translates these per… view at source ↗
Figure 2
Figure 2. Figure 2: Results for interpreting the initial stage of human tactile processing, in which features are mapped to perceptual attributes (Model 1). (A) The performance of the regression models, evaluated using Mean Squared Error (MSE) and R-squared (R 2 ) scores. A lower MSE and a higher R 2 indicate better regression performance. All results were obtained using Random Forest regression, except for the null model, wh… view at source ↗
read the original abstract

Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual representations remains poorly understood. This knowledge gap hinders the integration of touch in digital environments and the development of robots capable of human-like tactile perception. Here, we present an interpretable computational framework for modeling human material perception and recognition using multisensory touch data. Our framework comprises three interconnected models: Model 1 maps finger-surface interaction features to psychophysical sensory attributes, Model 2 classifies materials based on these perceptual representations, and Model 3 directly classifies materials from tactile features. The results showed that combining information from pressing, static contact, and sliding interactions improves prediction accuracy, and that thermal cues are particularly informative for both perceptual modeling and material classification. These findings highlight the importance of thermal and compliance cues, which remain underrepresented in current robotic fingers and haptic displays. Incorporating such cues may enhance artificial systems' ability to approximate human material perception and guide the design of more perceptually grounded haptic interfaces.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript presents an interpretable computational framework with three interconnected models for human material perception from multisensory tactile data: Model 1 maps finger-surface interaction features (from pressing, static contact, and sliding) to psychophysical sensory attributes; Model 2 classifies materials from those perceptual representations; and Model 3 classifies materials directly from the tactile features. The central results indicate that combining the three interaction types improves prediction accuracy and that thermal cues are particularly informative for both perceptual modeling and material classification.

Significance. If the reported results hold, the work is significant for robotics and haptics because it quantifies the contribution of underrepresented thermal and compliance cues to human-like material perception and provides an interpretable pipeline that could guide the design of more perceptually grounded artificial tactile systems. The use of cross-validation, feature ablation, and participant-level consistency checks constitutes a strength that directly supports the multisensory-combination claim.

minor comments (2)
  1. [Abstract] Abstract: the claim of improved accuracy is stated without any numerical values, error bars, dataset size, or validation details, which reduces the standalone readability of the abstract even though the full text supplies these.
  2. The weakest assumption noted in the stress-test (that chosen tactile features and psychophysical attributes capture the low-level to perceptual mapping without significant sensor or participant bias) is addressed by the reported participant-level checks, so it does not constitute a load-bearing concern.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, including the recognition of the framework's interpretability, the value of cross-validation and ablation analyses, and the potential significance for robotics and haptics. The recommendation for minor revision is noted.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical pipeline of three fitted models (feature-to-attribute mapping, attribute-based classification, and direct feature classification) evaluated via cross-validation and feature ablation on collected multisensory tactile data. No equations, derivations, or self-referential predictions appear in the abstract or described framework; results are reported as statistical outcomes from held-out data rather than by-construction identities. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked. The central claim rests on observable performance differences across interaction types and modalities, which are directly testable against the experimental dataset and participant responses.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no explicit free parameters, axioms, or invented entities are stated or derivable from the provided text.

pith-pipeline@v0.9.1-grok · 5702 in / 1000 out tokens · 19248 ms · 2026-06-29T06:57:12.812221+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

42 extracted references · 30 canonical work pages · 1 internal anchor

  1. [1]

    Klatzky, R. L. Haptic perception and its relation to action.Annu. Rev. Psychol.76, 227–250, DOI: https://doi.org/10.1146/ annurev-psych-011624-101129 (2025)

    2025

  2. [2]

    Lederman, S. J. & Klatzky, R. L. Hand movements: A window into haptic object recognition.Cogn. Psychol.19, 342–368, DOI: https://doi.org/10.1016/0010-0285(87)90008-9 (1987)

    work page doi:10.1016/0010-0285(87)90008-9 1987

  3. [3]

    P., Davis-Berg, E

    Callier, T., Saal, H. P., Davis-Berg, E. C. & Bensmaia, S. J. Kinematics of unconstrained tactile texture exploration.J. Neurophysiol.113, 3013–3020, DOI: 10.1152/jn.00703.2014 (2015). PMID: 25744883, https://doi.org/10.1152/jn.00703. 2014

    work page doi:10.1152/jn.00703.2014 2014

  4. [4]

    & Yamada, Y

    Okamoto, S., Nagano, H. & Yamada, Y . Psychophysical dimensions of tactile perception of textures.IEEE Transactions on Haptics6, 81–93, DOI: 10.1109/TOH.2012.32 (2013)

    work page doi:10.1109/toh.2012.32 2012

  5. [5]

    & Kaya, D

    Drewing, K., Weyel, C., Celebi, H. & Kaya, D. Systematic relations between affective and sensory material dimensions in touch.IEEE Transactions on Haptics11, 611–622, DOI: 10.1109/TOH.2018.2836427 (2018)

    work page doi:10.1109/toh.2018.2836427 2018

  6. [6]

    & Young, F

    Hollins, M., Faldowski, R., Rao, S. & Young, F. Perceptual dimensions of tactile surface texture: A multidimensional scaling analysis.Percept.&Pscyhophysics54, 697–705, DOI: 10.3758/BF03211795 (1993)

    work page doi:10.3758/bf03211795 1993

  7. [7]

    Bergmann Tiest, W. M. & Kappers, A. M. Analysis of haptic perception of materials by multidimensional scaling and physical measurements of roughness and compressibility.Acta Psychol.121, 1–20, DOI: https://doi.org/10.1016/j.actpsy. 2005.04.005 (2006)

    work page doi:10.1016/j.actpsy 2005

  8. [8]

    Skedung, L., Harris, K., Collier, E. S. & Rutland, M. The finishing touches: the role of friction and roughness in haptic perception of surface coatings.Exp. Brain Res.238, 1511–1524, DOI: 10.1007/s00221-020-05831-w (2020). 9.Vardar, Y ., Wallraven, C. & Kuchenbecker, K. J. Fingertip interaction metrics correlate with visual and haptic perception of real ...

    work page doi:10.1007/s00221-020-05831-w 2020

  9. [9]

    J., Craig, J

    Yoshioka, T., Bensmaia, S. J., Craig, J. C. & Hsiao, S. S. Texture perception through direct and indirect touch: An analysis of perceptual space for tactile textures in two modes of exploration.Somatosens. Mot. Res.24, 53–70, DOI: 10.1080/08990220701318163 (2009)

    work page doi:10.1080/08990220701318163 2009

  10. [10]

    & Steinbach, E

    Strese, M., Brudermueller, L., Kirsch, J. & Steinbach, E. Haptic material analysis and classification inspired by human exploratory procedures.IEEE Transactions on Haptics13, 404–424, DOI: 10.1109/TOH.2019.2952118 (2020)

    work page doi:10.1109/toh.2019.2952118 2019

  11. [11]

    Devillard, A. W. M., Ramasamy, A., Cheng, X., Faux, D. & Burdet, E. Tactile, audio, and visual dataset during bare finger interaction with textured surfaces.Sci. Data12, 484 (2025)

    2025

  12. [12]

    Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks

    Erickson, Z., Chernova, S. & Kemp, C. C. Semi-supervised haptic material recognition for robots using generative adversarial networks (2017). 1707.02796

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  13. [13]

    & Quinn, J

    Shuvo, M., Coleman, S., Kerr, D. & Quinn, J. Material classification using visio-tactile sensor for haptic feedback generation. 1–20 (2025)

    2025

  14. [14]

    Fishel, J. A. & Loeb, G. E. Bayesian exploration for intelligent identification of textures.Front. NeuroroboticsV olume 6 - 2012, DOI: 10.3389/fnbot.2012.00004 (2012). 9/12

    work page doi:10.3389/fnbot.2012.00004 2012

  15. [15]

    & Takahashi, S

    Saga, S., Agatsuma, S., Vasilache, S. & Takahashi, S. Machine learning-based classification and generation of vibrotactile information.Int. J. On Adv. Networks Serv.13, 115–124 (2020)

    2020

  16. [16]

    & Jiang, H

    Zhang, P., Du, D. & Jiang, H. Classification of visual-tactile fusion object attributes based on the gamp attention mechanism. In2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 6, 1279–1282 (2024)

    2024

  17. [17]

    Multi.18, 2407–2416 (2016)

    Zheng, H.et al.Deep learning for surface material classification using haptic and visual information.Trans. Multi.18, 2407–2416 (2016)

    2016

  18. [18]

    Richardson, B. A. & Kuchenbecker, K. J. Learning to predict perceptual distributions of haptic adjectives.Front. NeuroroboticsV olume 13 - 2019, DOI: 10.3389/fnbot.2019.00116 (2020)

    work page doi:10.3389/fnbot.2019.00116 2019

  19. [19]

    & Chaudhuri, S

    K, P., Chaudhuri, S. & Chaudhuri, S. Perceptnet: Learning perceptual similarity of haptic textures in presence of unorderable triplets. In2019 IEEE World Haptics Conference (WHC), 163–168, DOI: 10.1109/WHC.2019.8816141 (2019)

    work page doi:10.1109/whc.2019.8816141 2019

  20. [20]

    & Park, G

    Lim, C., Kim, G., Kang, S.-Y ., Seifi, H. & Park, G. Can a machine feel vibrations?: Predicting roughness and emotional responses to vibration tactons via a neural network.IEEE Transactions on Haptics18, 542–555, DOI: 10.1109/TOH.2025. 3568804 (2025)

    work page doi:10.1109/toh.2025 2025

  21. [21]

    Baumgartner, E., Wiebel, C. B. & Gegenfurtner, K. R. Visual and haptic representations of material properties.Multisensory research26 5, 429–55 (2013)

    2013

  22. [22]

    & Steinbach, E

    Strese, M., Schuwerk, C., Iepure, A. & Steinbach, E. Multimodal feature-based surface material classification.IEEE Transactions on Haptics10, 226–239, DOI: 10.1109/TOH.2016.2625787 (2017)

    work page doi:10.1109/toh.2016.2625787 2016

  23. [23]

    & Kuchenbecker, K

    Richardson, B., Vardar, Y ., Wallraven, C. & Kuchenbecker, K. Learning to feel textures: Predicting perceptual similarities from unconstrained finger-surface interactions.IEEE Transactions on Haptics15, 705–717, DOI: 10.1109/TOH.2022. 3212701 (2022)

    work page doi:10.1109/toh.2022 2022

  24. [24]

    & Vardar, Y

    Balasubramanian, J., Kodak, B. & Vardar, Y . Sens3: Multisensory database of finger-surface interactions and corresponding sensations. In Kajimoto, H.et al.(eds.)Haptics, vol. 1 ofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 262–277, DOI: 10.1007/978-3-031-70058-3_21 ...

    work page doi:10.1007/978-3-031-70058-3_21 2025

  25. [25]

    & Hodges, J

    Fix, E. & Hodges, J. L. Discriminatory analysis—Nonparametric discrimination: Consistency properties. Tech. Rep. 21-49-004, USAF School of Aviation Medicine, Randolph Field, Texas (1951)

    1951

  26. [26]

    Cover, T. M. & Hart, P. E. Nearest neighbor pattern classification.IEEE Transactions on Inf. Theory13, 21–27, DOI: 10.1109/TIT.1967.1053964 (1967). CiteSeerX: 10.1.1.68.2616. 28.Cortes, C. & Vapnik, V . Support-vector networks.Mach. Learn.20, 273–297, DOI: 10.1007/BF00994018 (1995)

    work page doi:10.1109/tit.1967.1053964 1967

  27. [27]

    & Zou, L

    Wang, A. & Zou, L. Persistent homology for structural characterization in disordered systems.Phys. Rev. E111, 045306, DOI: 10.1103/PhysRevE.111.045306 (2025)

    work page doi:10.1103/physreve.111.045306 2025

  28. [28]

    Ho, T. K. Random decision forests. InProceedings of the 3rd International Conference on Document Analysis and Recognition, 278–282 (Montreal, QC, 1995). Archived from the original on 17 April 2016. Retrieved 5 June 2016

    1995

  29. [29]

    Ho, T. K. The random subspace method for constructing decision forests.IEEE Transactions on Pattern Analysis Mach. Intell.20, 832–844, DOI: 10.1109/34.709601 (1998). Bibcode: 1998ITPAM..20..832T

    work page doi:10.1109/34.709601 1998

  30. [30]

    & Friedman, J

    Hastie, T., Tibshirani, R. & Friedman, J. H.10. Boosting and Additive Trees, 337–384 (Springer, New York, 2009), 2 edn. Archived from the original on 10 November 2009

    2009

  31. [31]

    & Rokach, L

    Sagi, O. & Rokach, L. Approximating xgboost with an interpretable decision tree.Inf. Sci.572, 522–542, DOI: https://doi.org/10.1016/j.ins.2021.05.055 (2021)

    work page doi:10.1016/j.ins.2021.05.055 2021

  32. [32]

    Mathematics of Control, Signals and Systems , author =

    Cybenko, G. Approximation by superpositions of a sigmoidal function.Math. Control. Signals, Syst.2, 303–314, DOI: 10.1007/BF02551274 (1989)

    work page doi:10.1007/bf02551274 1989

  33. [33]

    & Sosso, G

    Wang, A. & Sosso, G. C. Graph-based descriptors for condensed matter.Phys. Rev. E111, 064302, DOI: 10.1103/ PhysRevE.111.064302 (2025)

    2025

  34. [34]

    Material recognition based on thermal cues: Mechanisms and applications.Temperature5, 36–55, DOI: 10.1080/23328940.2017.1372042 (2017)

    Ho, H.-N. Material recognition based on thermal cues: Mechanisms and applications.Temperature5, 36–55, DOI: 10.1080/23328940.2017.1372042 (2017)

    work page doi:10.1080/23328940.2017.1372042 2017

  35. [35]

    & Havenith, G

    Filingeri, D., Fournet, D., Hodder, S. & Havenith, G. Why wet feels wet? a neurophysiological model of human cutaneous wetness sensitivity.J. Neurophysiol.112, 1457–1469, DOI: 10.1152/jn.00120.2014 (2014). 10/12

    work page doi:10.1152/jn.00120.2014 2014

  36. [36]

    Bergmann Tiest, W. M. Tactual perception of material properties.Vis. Res.50, 2775–2782, DOI: https://doi.org/10.1016/j. visres.2010.10.005 (2010). Perception and Action: Part I

    work page doi:10.1016/j 2010

  37. [37]

    & Kuchenbecker, K

    Nam, S., Vardar, Y ., Gueorguiev, D. & Kuchenbecker, K. J. Physical variables underlying tactile stickiness during fingerpad detachment.Front. Neurosci.V olume 14 - 2020, DOI: 10.3389/fnins.2020.00235 (2020)

    work page doi:10.3389/fnins.2020.00235 2020

  38. [38]

    Lepora, N. F. Tactile robotics: Past and future.The Int. J. Robotics Res.0, 02783649261421615, DOI: 10.1177/ 02783649261421615 (2026). https://journals.sagepub.com/doi/pdf/10.1177/02783649261421615

    work page doi:10.1177/02783649261421615 2026

  39. [39]

    Chen, Z., Huang, Y ., Zhang, B., Sun, D. & Yu, X. Deformable materials and structures in wearable haptic interfaces.Nat. Rev. Mater.DOI: 10.1038/s41578-025-00877-0 (2026)

    work page doi:10.1038/s41578-025-00877-0 2026

  40. [40]

    & Visell, Y

    Biswas, S. & Visell, Y . Emerging material technologies for haptics.Adv. Mater. Technol.4, 1900042, DOI: https: //doi-org.tudelft.idm.oclc.org/10.1002/admt.201900042 (2019). https://advanced-onlinelibrary-wiley-com.tudelft.idm.oclc. org/doi/pdf/10.1002/admt.201900042

    work page doi:10.1002/admt.201900042 2019

  41. [41]

    & Anh, K

    Vardar, Y ., Jeong, H. & Anh, K. Multimodal haptic device enabling naturalistic surface rendering during active touch (2025)

    2025

  42. [42]

    Kodak, B. L. & Vardar, Y . Feelpen: A haptic stylus displaying multimodal texture feels on touchscreens.IEEE/ASME Transactions on Mechatronics28, 2930–2940, DOI: 10.1109/TMECH.2023.3264787 (2023). Acknowledgements This study was partially funded by the Dutch Research Council (NWO) under the VENI scheme (ID: 19153) and the European Research Council (ERC) u...

    work page doi:10.1109/tmech.2023.3264787 2023