Why Modeling Human Haptic Material Perception with AI Is Difficult

Yasemin Vardar

arxiv: 2605.16602 · v1 · pith:WI7BJQ53new · submitted 2026-05-15 · 💻 cs.HC · cs.AI

Why Modeling Human Haptic Material Perception with AI Is Difficult

Yasemin Vardar This is my paper

Pith reviewed 2026-05-20 15:50 UTC · model grok-4.3

classification 💻 cs.HC cs.AI

keywords haptic perceptionmaterial recognitiontactile sensingAI challengeshaptic datasetsperceptual benchmarksmodel interpretabilityhuman touch

0 comments

The pith

AI modeling of human touch perception of materials is blocked by scarce data, missing benchmarks, and limited model interpretability.

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

The paper argues that while touch is central to how people recognize materials, AI systems struggle to build similar capabilities because of three main limits. First, there are not enough large, varied, and balanced collections of tactile data. Second, researchers lack common platforms and tests that measure perceptual accuracy in consistent ways. Third, current models have trouble handling the interactive and multimodal nature of touch and are hard to interpret. If these bottlenecks are removed, AI could support better interactive devices and give new clues about the biological processes that turn physical contact into meaningful sensations.

Core claim

This position paper argues that progress at the intersection of AI and haptics is constrained by three key bottlenecks: the scarcity of large, diverse, and balanced haptic datasets; the lack of standardized evaluation platforms and perceptual benchmarks; and limitations in model capacity and interpretability when applied to tactile perception. These challenges impede generalization, reproducibility, and scientific insight into human touch. The author reviews emerging strategies to address them and highlights opportunities for coordinated, cross-disciplinary efforts to advance AI systems that perform robust haptic perception while also contributing to a deeper understanding of human touch.

What carries the argument

The three bottlenecks of insufficient haptic datasets, absent standardized evaluation platforms with perceptual benchmarks, and restricted model capacity plus interpretability for tactile signals.

If this is right

Larger and more balanced haptic datasets would support better generalization across materials and interaction conditions.
Standardized evaluation platforms and perceptual benchmarks would increase reproducibility across studies.
Improved model capacity and interpretability would generate clearer scientific insight into how tactile signals become perceptual representations.
Coordinated cross-disciplinary work on these issues would produce AI systems capable of robust haptic perception in interactive applications.

Where Pith is reading between the lines

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

Progress on these bottlenecks could also benefit fields such as robotics that rely on realistic touch feedback for object handling.
The position implies that current AI limitations are largely practical and addressable rather than arising from an inherent gap between statistical learning and human sensory processing.
Future tests could check whether fixing the three bottlenecks narrows the performance difference between AI and human observers on material recognition tasks.

Load-bearing premise

The three bottlenecks in data, benchmarks, and interpretability are the main and most fixable barriers, and tackling them together will improve both AI performance and scientific knowledge of touch rather than other limits such as sensor hardware or basic mismatches with biological perception.

What would settle it

If researchers release large diverse haptic datasets, adopt shared evaluation platforms with perceptual benchmarks, and train more interpretable models yet still see no gains in generalization or matches to human material judgments, the claim that these three factors are the primary constraints would be weakened.

Figures

Figures reproduced from arXiv: 2605.16602 by Yasemin Vardar.

read the original abstract

Touch plays a central role in how humans perceive and recognize materials through physical contact. Despite decades of research, the mechanisms by which tactile signals are transformed into meaningful perceptual representations remain poorly understood, limiting the design of interactive systems and intelligent agents with human-like haptic perception. Recent advances in artificial intelligence (AI) offer new opportunities to model and exploit tactile data; however, haptics presents fundamental challenges for contemporary AI due to its interaction-dependent, multimodal nature. This position paper argues that progress at the intersection of AI and haptics is constrained by three key bottlenecks: (1) the scarcity of large, diverse, and balanced haptic datasets; (2) the lack of standardized evaluation platforms and perceptual benchmarks; and (3) limitations in model capacity and interpretability when applied to tactile perception. I discuss how these challenges impede generalization, reproducibility, and scientific insight into human touch and review emerging strategies to address them. This paper highlights opportunities for coordinated, cross-disciplinary efforts to advance AI systems that not only perform robust haptic perception but also contribute to a deeper understanding of human touch.

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.

Desk Editor's Note private letter to a colleague

This position paper usefully organizes the bottlenecks in haptic AI but doesn't establish why its three are more important than hardware or active sensing factors.

read the letter

The paper's core point is that AI struggles with human-like haptic material perception mainly because of three bottlenecks: scarce large and balanced datasets, no standardized evaluation platforms or perceptual benchmarks, and limits in how well current models can handle and interpret tactile data. It frames these as the main things holding back both practical systems and deeper scientific insight. What stands out as useful is the way it organizes well-known issues from the literature into this clear structure and then reviews some emerging ways to tackle them, like better data collection or cross-field collaborations. That synthesis can help people see the big picture in haptic AI without having to hunt through many papers. The softer spot is that it doesn't really test or compare these bottlenecks to other possible limits, such as the physical capabilities of tactile sensors or the active, context-dependent way people explore materials by touch. If those turn out to be more fundamental, then fixing datasets and benchmarks might not deliver the expected advances. The paper treats the three as primary without much evidence or discussion to back that up, which makes the position a bit more declarative than demonstrated. This kind of overview is aimed at researchers in human-computer interaction and haptics who want to understand the current barriers. Someone planning work in touch interfaces or AI for robotics would find it a reasonable starting point for thinking about priorities. It shows honest engagement with the challenges in the field, so it deserves a serious referee. I'd recommend sending it to peer review to get feedback that could strengthen the case for focusing on those specific issues.

Referee Report

2 major / 2 minor

Summary. This position paper argues that progress in modeling human haptic material perception with AI is constrained by three key bottlenecks: (1) scarcity of large, diverse, and balanced haptic datasets; (2) lack of standardized evaluation platforms and perceptual benchmarks; and (3) limitations in model capacity and interpretability for tactile perception. It discusses the implications of these challenges for generalization, reproducibility, and scientific insight into human touch, reviews emerging strategies to address them, and calls for coordinated cross-disciplinary efforts to advance both practical AI systems and fundamental understanding of tactile perception.

Significance. If the three bottlenecks are shown to be primary, the paper could usefully frame research priorities at the AI-haptics intersection by emphasizing data collection, benchmark standardization, and interpretability needs. This framing might encourage collaborative initiatives that simultaneously improve interactive systems and contribute to perceptual science. However, the absence of quantitative evidence, systematic review, or comparative analysis reduces its immediate utility as a definitive roadmap.

major comments (2)

[Abstract and introduction] The central claim (abstract; opening paragraphs of the main text) that the three bottlenecks are the dominant constraints lacks comparative discussion of why they outweigh other factors such as physical limits of current tactile sensors (spatial/temporal resolution, force range, contact area) or the active, exploratory, context-dependent character of human haptic perception. This is load-bearing for the recommendation of coordinated efforts on data and benchmarks, as the argument treats the listed items as primary without evidence that progress on them would not be gated by unaddressed hardware or sensorimotor-loop mismatches.
[Discussion of strategies] The review of emerging strategies (later sections) does not include any assessment of their likely effectiveness or references to preliminary results from related work showing that addressing data scarcity, benchmarks, or interpretability would measurably improve modeling of human-like tactile perception; this leaves the proposed path forward somewhat speculative relative to the strength of the bottleneck claims.

minor comments (2)

Clarify early in the manuscript that this is a position paper rather than an empirical or review study to align reader expectations with the argumentative style and lack of new data.
Ensure citations to existing haptic datasets and AI-haptics surveys are comprehensive and current; a brief table summarizing key available datasets would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our position paper. The comments help clarify how to better justify the central claims and strengthen the discussion of strategies. We address each major comment below, indicating where revisions will be made.

read point-by-point responses

Referee: [Abstract and introduction] The central claim (abstract; opening paragraphs of the main text) that the three bottlenecks are the dominant constraints lacks comparative discussion of why they outweigh other factors such as physical limits of current tactile sensors (spatial/temporal resolution, force range, contact area) or the active, exploratory, context-dependent character of human haptic perception. This is load-bearing for the recommendation of coordinated efforts on data and benchmarks, as the argument treats the listed items as primary without evidence that progress on them would not be gated by unaddressed hardware or sensorimotor-loop mismatches.

Authors: We agree that a comparative discussion would strengthen the argument. The manuscript focuses on bottlenecks that are particularly acute for AI modeling of haptic perception, as sensor hardware advances occur in a parallel track and the active, exploratory nature of touch is already reflected in many existing tactile datasets and interaction protocols. Nevertheless, we will add a concise paragraph in the introduction that acknowledges sensor limitations and sensorimotor-loop considerations, then explains why the three listed bottlenecks remain primary for AI progress: they directly constrain generalization, reproducibility, and the ability to derive scientific insight from models, even as hardware improves. This revision will make the load-bearing claim more robust without altering the paper's core position. revision: yes
Referee: [Discussion of strategies] The review of emerging strategies (later sections) does not include any assessment of their likely effectiveness or references to preliminary results from related work showing that addressing data scarcity, benchmarks, or interpretability would measurably improve modeling of human-like tactile perception; this leaves the proposed path forward somewhat speculative relative to the strength of the bottleneck claims.

Authors: We accept that the strategies section would benefit from greater specificity. As a position paper our goal is to identify opportunities rather than perform a systematic evaluation, yet we can still reference existing preliminary results (for example, performance gains reported in tactile classification tasks when dataset size or diversity increases, or early benchmark-driven improvements in cross-material generalization). In the revision we will expand the discussion to include brief assessments of likely effectiveness drawn from cited related work, along with caveats where evidence remains limited. This will reduce speculation while preserving the forward-looking character of the paper. revision: yes

Circularity Check

0 steps flagged

No circularity; position paper lacks derivations, equations, or self-referential reductions

full rationale

This is a position paper that enumerates three bottlenecks in AI-haptics research based on stated observations about data scarcity, missing benchmarks, and model limitations. No equations, first-principles derivations, fitted parameters, or predictions appear in the provided text or abstract. The central claims rest on external literature and qualitative assessment rather than any self-definitional loop, fitted-input-as-prediction, or load-bearing self-citation chain that reduces the argument to its own inputs by construction. The paper is therefore self-contained as a discussion of challenges and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces no mathematical models, fitted parameters, background axioms, or postulated entities; it is a qualitative discussion of field-level obstacles without technical derivations.

pith-pipeline@v0.9.0 · 5709 in / 1192 out tokens · 54739 ms · 2026-05-20T15:50:08.357215+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

[1]

Balasubramanian, Bence L

Jagan K. Balasubramanian, Bence L. Kodak, and Yasemin Vardar. 2025. SENS3: Multisensory Database of Finger-Surface Interactions and Corre- sponding Sensations. InHaptics (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 1), Hiroyuki Kajimoto, Pedro Lopes, Claudio Pac...

work page doi:10.1007/978-3-031-70058-3_21 2025
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Balasubramanian, Daan M

Jagan K. Balasubramanian, Daan M. Pool, and Yasemin Vardar. 2026. Sliding speed influences electrovibration-induced finger friction dynamics on touchscreens.Tribology International213 (2026), 111054. doi:10.1016/j.triboint.2025.111054

work page doi:10.1016/j.triboint.2025.111054 2026
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Quinn Begelinger and Yasemin Vardar. 2026. Generating Tactile Textures from Perceptual Descriptors with Diffusion Models: A Feasibility Study. InExtended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), April 13–17, 2026, Barcelona, Spain. doi:10.1145/3772363.3799381

work page doi:10.1145/3772363.3799381 2026
[4]

Shaoyu Cai, Lu Zhao, Yuki Ban, Takuji Narumi, Yue Liu, and Kening Zhu. 2022. GAN-based image-to-friction generation for tactile simulation of fabric material.Computers & Graphics102 (2022), 460–473. doi:10.1016/j.cag.2021.09.007

work page doi:10.1016/j.cag.2021.09.007 2022
[5]

Heather Culbertson, Juan José López Delgado, and Katherine J Kuchenbecker. 2014. One hundred data-driven haptic texture models and open-source methods for rendering on 3D objects. In2014 IEEE Haptics Symposium (HAPTICS). IEEE, 319–325

work page 2014
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Alexis W. M. Devillard, Aruna Ramasamy, Xiaoxiao Cheng, Damien Faux, and Etienne Burdet. 2025. Tactile, Audio, and Visual Dataset During Bare Finger Interaction with Textured Surfaces.Scientific Data12 (2025), 484. doi:10.1038/s41597-025-04670-0

work page doi:10.1038/s41597-025-04670-0 2025
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Faraz Faruqi, Maxine Perroni-Scharf, Jaskaran Singh Walia, Yunyi Zhu, Shuyue Feng, Donald Degraen, and Stefanie Mueller. 2025. TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, A...

work page doi:10.1145/3706598.3713740 2025
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Waseem Hassan, Arsen Abdulali, and Seokhee Jeon. 2020. Authoring New Haptic Textures Based on Interpolation of Real Textures in Affective Space.IEEE Transactions on Industrial Electronics67, 1 (2020), 667–676. doi:10.1109/TIE.2019.2914572

work page doi:10.1109/tie.2019.2914572 2020
[9]

Okamura, and Jeannette Bohg

Negin Heravi, Heather Culbertson, Allison M. Okamura, and Jeannette Bohg. 2024. Development and Evaluation of a Learning-Based Model for Real-Time Haptic Texture Rendering.IEEE Transactions on Haptics17, 4 (2024), 705–716. doi:10.1109/TOH.2024.3382258

work page doi:10.1109/toh.2024.3382258 2024
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Arata Jingu, Easa AliAbbasi, Paul Strohmeier, and Jürgen Steimle. 2025. Scene2Hap: Combining LLMs and Physical Modeling for Automatically Generating Vibrotactile Signals for Full VR Scenes. arXiv:2504.19611 [cs.HC] https://arxiv.org/abs/2504.19611

work page arXiv 2025
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Johansson and J

Roland S. Johansson and J. Randall Flanagan. 2009. Coding and use of tactile signals from the fingertips in object manipulation tasks.Nature Reviews Neuroscience10, 5 (2009), 345–359

work page 2009
[12]

Fontaine, Stefanos Nikolaidis, and Heather Culbertson

Shihan Lu, Mianlun Zheng, Matthew C. Fontaine, Stefanos Nikolaidis, and Heather Culbertson. 2022. Preference-Driven Texture Modeling Through Interactive Generation and Search.IEEE Transactions on Haptics15, 3 (2022), 508–520. doi:10.1109/TOH.2022.3173935

work page doi:10.1109/toh.2022.3173935 2022
[13]

Ratschat, Laura Marchal-Crespo, and Yasemin Vardar

Rubén Martín-Rodríguez, Alexandre L. Ratschat, Laura Marchal-Crespo, and Yasemin Vardar. 2025. Tactile Weight Rendering: A Review for Researchers and Developers.IEEE Transactions on Haptics18, 1 (Jan. 2025), 93–109. doi:10.1109/toh.2024.3453894

work page doi:10.1109/toh.2024.3453894 2025
[14]

Shogo Okamoto, Hikaru Nagano, and Yoji Yamada. 2013. Psychophysical Dimensions of Tactile Perception of Textures.IEEE Transactions on Haptics 6, 1 (2013), 81–93. doi:10.1109/TOH.2012.32

work page doi:10.1109/toh.2012.32 2013
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IEEE Transactions on Haptics 15, 705–717

Benjamin A. Richardson, Yasemin Vardar, Christian Wallraven, and Katherine J. Kuchenbecker. 2022. Learning to Feel Textures: Predicting Perceptual Similarities From Unconstrained Finger-Surface Interactions.IEEE Transactions on Haptics15, 4 (2022), 705–717. doi:10.1109/TOH.2022.3212701 Why Modeling Human Haptic Material Perception with AI Is Difficult 5

work page doi:10.1109/toh.2022.3212701 2022
[16]

Oliver Schneider, Bruno Fruchard, Dennis Wittchen, Bibhushan Raj Joshi, Georg Freitag, Donald Degraen, and Paul Strohmeier. 2022. Sustainable Haptic Design: Improving Collaboration, Sharing, and Reuse in Haptic Design Research. InExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA, USA)(CHI EA ’22). Associat...

work page doi:10.1145/3491101.3503734 2022
[17]

Matti Strese, Yannik Boeck, and Eckehard Steinbach. 2017. Content-based surface material retrieval. In2017 IEEE World Haptics Conference (WHC). IEEE, 352–357

work page 2017
[18]

Maciej Stroinski, Kamil Kwarciak, Mateusz Kowalewski, Daria Hemmerling, William Frier, and Orestis Georgiou. 2025. Text-to-Haptics: Enhancing Multisensory Storytelling through Emotionally Congruent Midair Haptics.Advanced Intelligent Systems7, 4 (2025), 2400758. arXiv:https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202400758 doi:10.1002/aisy...

work page doi:10.1002/aisy.202400758 2025
[19]

Youjin Sung, Kevin John, Sang Ho Yoon, and Hasti Seifi. 2025. HapticGen: Generative Text-to-Vibration Model for Streamlining Haptic Design. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, Article 495, 24 pages. doi:10.1145/3706598.3713609

work page doi:10.1145/3706598.3713609 2025
[20]

Matteo Toscani and Anna Metzger. 2022. A Database of Vibratory Signals from Free Haptic Exploration of Natural Material Textures and Perceptual Judgments (ViPer): Analysis of Spectral Statistics. InHaptics: Science, Technology, Applications, Hasti Seifi, Astrid M. L. Kappers, Oliver Schneider, Knut Drewing, Claudio Pacchierotti, Alireza Abbasimoshaei, Gij...

work page 2022
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Yusuke Ujitoko and Yuki Ban. 2018. Vibrotactile Signal Generation from Texture Images or Attributes Using Generative Adversarial Network. In Haptics: Science, Technology, and Applications, Domenico Prattichizzo, Hiroyuki Shinoda, Hong Z. Tan, Emanuele Ruffaldi, and Antonio Frisoli (Eds.). Springer International Publishing, Cham, 25–36

work page 2018
[22]

Michael Wiertlewski and Vincent Hayward. 2012. Mechanical behavior of the fingertip in the range of frequencies and displacements relevant to touch.Journal of Biomechanics45, 11 (2012), 1869–1874. doi:10.1016/j.jbiomech.2012.05.045

work page doi:10.1016/j.jbiomech.2012.05.045 2012
[23]

Mingxin Zhang, Shun Terui, Yasutoshi Makino, and Hiroyuki Shinoda. 2025. TexSenseGAN: A User-Guided System for Optimizing Texture-Related Vibrotactile Feedback Using Generative Adversarial Network.IEEE Transactions on Haptics18, 2 (2025), 325–339. doi:10.1109/TOH.2025.3542424

work page doi:10.1109/toh.2025.3542424 2025
[24]

Li Zou and Yasemin Vardar. [n. d.]. Learning to Feel Materials from Multisensory Tactile Data via Interpretable Models. The Author Yasemin Vardar is an Associate Professor in the Department of Cognitive Robotics at TU Delft, The Netherlands, where she leads the Haptic Interface Technology Lab. She received her Ph.D. in Mechanical Engineering from Koç Univ...

work page 2018

[1] [1]

Balasubramanian, Bence L

Jagan K. Balasubramanian, Bence L. Kodak, and Yasemin Vardar. 2025. SENS3: Multisensory Database of Finger-Surface Interactions and Corre- sponding Sensations. InHaptics (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 1), Hiroyuki Kajimoto, Pedro Lopes, Claudio Pac...

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

[2] [2]

Balasubramanian, Daan M

Jagan K. Balasubramanian, Daan M. Pool, and Yasemin Vardar. 2026. Sliding speed influences electrovibration-induced finger friction dynamics on touchscreens.Tribology International213 (2026), 111054. doi:10.1016/j.triboint.2025.111054

work page doi:10.1016/j.triboint.2025.111054 2026

[3] [3]

Quinn Begelinger and Yasemin Vardar. 2026. Generating Tactile Textures from Perceptual Descriptors with Diffusion Models: A Feasibility Study. InExtended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA ’26), April 13–17, 2026, Barcelona, Spain. doi:10.1145/3772363.3799381

work page doi:10.1145/3772363.3799381 2026

[4] [4]

Shaoyu Cai, Lu Zhao, Yuki Ban, Takuji Narumi, Yue Liu, and Kening Zhu. 2022. GAN-based image-to-friction generation for tactile simulation of fabric material.Computers & Graphics102 (2022), 460–473. doi:10.1016/j.cag.2021.09.007

work page doi:10.1016/j.cag.2021.09.007 2022

[5] [5]

Heather Culbertson, Juan José López Delgado, and Katherine J Kuchenbecker. 2014. One hundred data-driven haptic texture models and open-source methods for rendering on 3D objects. In2014 IEEE Haptics Symposium (HAPTICS). IEEE, 319–325

work page 2014

[6] [6]

Alexis W. M. Devillard, Aruna Ramasamy, Xiaoxiao Cheng, Damien Faux, and Etienne Burdet. 2025. Tactile, Audio, and Visual Dataset During Bare Finger Interaction with Textured Surfaces.Scientific Data12 (2025), 484. doi:10.1038/s41597-025-04670-0

work page doi:10.1038/s41597-025-04670-0 2025

[7] [7]

Faraz Faruqi, Maxine Perroni-Scharf, Jaskaran Singh Walia, Yunyi Zhu, Shuyue Feng, Donald Degraen, and Stefanie Mueller. 2025. TactStyle: Generating Tactile Textures with Generative AI for Digital Fabrication. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, A...

work page doi:10.1145/3706598.3713740 2025

[8] [8]

Waseem Hassan, Arsen Abdulali, and Seokhee Jeon. 2020. Authoring New Haptic Textures Based on Interpolation of Real Textures in Affective Space.IEEE Transactions on Industrial Electronics67, 1 (2020), 667–676. doi:10.1109/TIE.2019.2914572

work page doi:10.1109/tie.2019.2914572 2020

[9] [9]

Okamura, and Jeannette Bohg

Negin Heravi, Heather Culbertson, Allison M. Okamura, and Jeannette Bohg. 2024. Development and Evaluation of a Learning-Based Model for Real-Time Haptic Texture Rendering.IEEE Transactions on Haptics17, 4 (2024), 705–716. doi:10.1109/TOH.2024.3382258

work page doi:10.1109/toh.2024.3382258 2024

[10] [10]

Arata Jingu, Easa AliAbbasi, Paul Strohmeier, and Jürgen Steimle. 2025. Scene2Hap: Combining LLMs and Physical Modeling for Automatically Generating Vibrotactile Signals for Full VR Scenes. arXiv:2504.19611 [cs.HC] https://arxiv.org/abs/2504.19611

work page arXiv 2025

[11] [11]

Johansson and J

Roland S. Johansson and J. Randall Flanagan. 2009. Coding and use of tactile signals from the fingertips in object manipulation tasks.Nature Reviews Neuroscience10, 5 (2009), 345–359

work page 2009

[12] [12]

Fontaine, Stefanos Nikolaidis, and Heather Culbertson

Shihan Lu, Mianlun Zheng, Matthew C. Fontaine, Stefanos Nikolaidis, and Heather Culbertson. 2022. Preference-Driven Texture Modeling Through Interactive Generation and Search.IEEE Transactions on Haptics15, 3 (2022), 508–520. doi:10.1109/TOH.2022.3173935

work page doi:10.1109/toh.2022.3173935 2022

[13] [13]

Ratschat, Laura Marchal-Crespo, and Yasemin Vardar

Rubén Martín-Rodríguez, Alexandre L. Ratschat, Laura Marchal-Crespo, and Yasemin Vardar. 2025. Tactile Weight Rendering: A Review for Researchers and Developers.IEEE Transactions on Haptics18, 1 (Jan. 2025), 93–109. doi:10.1109/toh.2024.3453894

work page doi:10.1109/toh.2024.3453894 2025

[14] [14]

Shogo Okamoto, Hikaru Nagano, and Yoji Yamada. 2013. Psychophysical Dimensions of Tactile Perception of Textures.IEEE Transactions on Haptics 6, 1 (2013), 81–93. doi:10.1109/TOH.2012.32

work page doi:10.1109/toh.2012.32 2013

[15] [15]

IEEE Transactions on Haptics 15, 705–717

Benjamin A. Richardson, Yasemin Vardar, Christian Wallraven, and Katherine J. Kuchenbecker. 2022. Learning to Feel Textures: Predicting Perceptual Similarities From Unconstrained Finger-Surface Interactions.IEEE Transactions on Haptics15, 4 (2022), 705–717. doi:10.1109/TOH.2022.3212701 Why Modeling Human Haptic Material Perception with AI Is Difficult 5

work page doi:10.1109/toh.2022.3212701 2022

[16] [16]

Oliver Schneider, Bruno Fruchard, Dennis Wittchen, Bibhushan Raj Joshi, Georg Freitag, Donald Degraen, and Paul Strohmeier. 2022. Sustainable Haptic Design: Improving Collaboration, Sharing, and Reuse in Haptic Design Research. InExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA, USA)(CHI EA ’22). Associat...

work page doi:10.1145/3491101.3503734 2022

[17] [17]

Matti Strese, Yannik Boeck, and Eckehard Steinbach. 2017. Content-based surface material retrieval. In2017 IEEE World Haptics Conference (WHC). IEEE, 352–357

work page 2017

[18] [18]

Maciej Stroinski, Kamil Kwarciak, Mateusz Kowalewski, Daria Hemmerling, William Frier, and Orestis Georgiou. 2025. Text-to-Haptics: Enhancing Multisensory Storytelling through Emotionally Congruent Midair Haptics.Advanced Intelligent Systems7, 4 (2025), 2400758. arXiv:https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202400758 doi:10.1002/aisy...

work page doi:10.1002/aisy.202400758 2025

[19] [19]

Youjin Sung, Kevin John, Sang Ho Yoon, and Hasti Seifi. 2025. HapticGen: Generative Text-to-Vibration Model for Streamlining Haptic Design. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, Article 495, 24 pages. doi:10.1145/3706598.3713609

work page doi:10.1145/3706598.3713609 2025

[20] [20]

Matteo Toscani and Anna Metzger. 2022. A Database of Vibratory Signals from Free Haptic Exploration of Natural Material Textures and Perceptual Judgments (ViPer): Analysis of Spectral Statistics. InHaptics: Science, Technology, Applications, Hasti Seifi, Astrid M. L. Kappers, Oliver Schneider, Knut Drewing, Claudio Pacchierotti, Alireza Abbasimoshaei, Gij...

work page 2022

[21] [21]

Yusuke Ujitoko and Yuki Ban. 2018. Vibrotactile Signal Generation from Texture Images or Attributes Using Generative Adversarial Network. In Haptics: Science, Technology, and Applications, Domenico Prattichizzo, Hiroyuki Shinoda, Hong Z. Tan, Emanuele Ruffaldi, and Antonio Frisoli (Eds.). Springer International Publishing, Cham, 25–36

work page 2018

[22] [22]

Michael Wiertlewski and Vincent Hayward. 2012. Mechanical behavior of the fingertip in the range of frequencies and displacements relevant to touch.Journal of Biomechanics45, 11 (2012), 1869–1874. doi:10.1016/j.jbiomech.2012.05.045

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