pith. sign in

arxiv: 2606.29948 · v1 · pith:V4ZQUC6Onew · submitted 2026-06-29 · 💻 cs.RO

Heterogeneous Tactile Transformer

Jianxin Bi , Qiang Wang , Jayaram Reddy , Kelvin Lin , Soibkhon Khajikhanov , Ruihan Gao , Harold Soh This is my paper

Pith reviewed 2026-06-30 05:50 UTC · model grok-4.3

classification 💻 cs.RO
keywords tactile sensingtransformerheterogeneous sensorsrepresentation learningcross-modal alignmentrobot manipulationmasked reconstructiontransfer learning
0
0 comments X

The pith

A transformer with sensor-specific encoders learns shared tactile representations from paired data across four different sensors.

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

Tactile sensors differ in hardware, so a model trained on one type cannot be applied directly to another. The paper builds the Heterogeneous Tactile Transformer with separate encoders for each sensor type plus one shared transformer trunk. It pretrains the model on 1.6 million paired frames from four sensors using two tasks: reconstructing masked inputs within each sensor and aligning features across paired sensors. The resulting representations then adapt to new perception and manipulation tasks and to sensors absent from pretraining.

Core claim

The Heterogeneous Tactile Transformer consists of sensor-specific encoders and a shared transformer trunk. It is pretrained with per-modality masked reconstruction together with cross-modal alignment between paired sensors on the Heterogeneous Paired Tactile dataset of 1.6M synchronized frames across four vision- and array-based sensors. Across distinct tactile perception and real-world manipulation tasks, this produces transferable representations that adapt to new tasks and previously unseen sensors.

What carries the argument

Sensor-specific encoders plus a shared transformer trunk, trained by per-modality masked reconstruction and cross-modal alignment on paired tactile frames.

If this is right

  • Representations transfer to new tactile perception and real-world manipulation tasks.
  • The model adapts to previously unseen sensors.
  • Pretraining on the HPT dataset supports learning contact-rich manipulation policies from data collected on varied sensors.
  • Sensor-specific encoders allow the shared trunk to process inputs from heterogeneous hardware without direct compatibility.

Where Pith is reading between the lines

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

  • The same pretraining approach could let researchers pool tactile data collected on different hardware in different labs without requiring matched sensor pairs for every new experiment.
  • Extending the method to align more than two sensors simultaneously might increase robustness when many sensor types are available.
  • The framework suggests a route to reduce the data-collection burden when deploying tactile sensing on new robot hardware.

Load-bearing premise

The combination of per-modality masked reconstruction and cross-modal alignment on the HPT paired dataset produces representations that genuinely generalize beyond the four sensors and tasks used in pretraining and evaluation.

What would settle it

Evaluating the pretrained model on a fifth tactile sensor absent from the HPT dataset and measuring whether its performance on a held-out manipulation task equals or exceeds a model trained from scratch on data from that fifth sensor.

Figures

Figures reproduced from arXiv: 2606.29948 by Harold Soh, Jayaram Reddy, Jianxin Bi, Kelvin Lin, Qiang Wang, Ruihan Gao, Soibkhon Khajikhanov.

Figure 1
Figure 1. Figure 1: Heterogeneous Tactile Transformer (HTT). HTT is pretrained on the Heterogeneous Paired Tactile (HPT) dataset, which consists of 1.6M synchronized paired frames across four distinct sensors with a UMI device. HTT adopts sensor-specific encoders and a shared transformer trunk as core modules. Data from each sensor is patchified, fed into a sensor-specific encoder, and forwarded to the shared transformer trun… view at source ↗
Figure 2
Figure 2. Figure 2: Force/slip data collection and dataset statistics. A1. A tactile sensor and a 6-D F/T sensor are mounted on a robot arm; a probe rig contacts the tactile sensor while synchronized tactile frames and ground-truth force are recorded for the force-estimation and slip-detection splits. A2. Example tactile frames collected with the four probe geometries. B1. The force range spans up to 40 N normal and 14 N shea… view at source ↗
Figure 3
Figure 3. Figure 3: Real World Toy Screw and Grasp Tofu Experiments. Left: Task setup and tactile image [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The four heterogeneous tactile sensors used in HPT pretraining, each mounted in the UMI [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example samples for 2 optical sensor encoders. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
read the original abstract

Tactile sensors are inherently heterogeneous: a model trained on one sensor cannot be directly used on another, which limits learning contact-rich manipulation policies from diverse tactile data at scale. To bridge this gap, we propose the Heterogeneous Tactile Transformer (HTT), a framework that learns shared tactile representations across heterogeneous sensors. HTT consists of sensor-specific encoders and a shared transformer trunk, and is pretrained with per-modality masked reconstruction together with cross-modal alignment between paired sensors. Pretraining uses our novel Heterogeneous Paired Tactile (HPT) dataset, containing 1.6M synchronized paired frames across four vision- and array-based tactile sensors. Across distinct tactile perception and real-world manipulation tasks, HTT is shown to learn transferable representations that adapt to new tasks and previously unseen sensors. Dataset, code, and model checkpoints will be released upon publication at https://jxbi1010.github.io/htt-gh-page/.

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

1 major / 0 minor

Summary. The manuscript introduces the Heterogeneous Tactile Transformer (HTT), consisting of sensor-specific encoders and a shared transformer trunk. It is pretrained via per-modality masked reconstruction and cross-modal alignment losses on the authors' new Heterogeneous Paired Tactile (HPT) dataset of 1.6M synchronized paired frames collected from four vision- and array-based tactile sensors. The central claim is that the resulting representations transfer successfully to distinct tactile perception tasks and real-world manipulation tasks, including adaptation to new tasks and previously unseen sensors.

Significance. If the transfer results are quantitatively supported, the work addresses a core practical barrier in tactile robotics: sensor heterogeneity that prevents pooling data across devices. Enabling cross-sensor transfer could allow larger-scale pretraining and more general policies. The planned public release of the HPT dataset, code, and checkpoints would further increase the contribution's value for reproducibility and follow-on research.

major comments (1)
  1. [Abstract] Abstract: the central claim that HTT 'is shown to learn transferable representations that adapt to new tasks and previously unseen sensors' is asserted without any quantitative metrics, baselines, ablation studies, or error analysis. This absence makes it impossible to evaluate whether the generalization result holds or is affected by post-hoc experimental choices.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and the opportunity to clarify the presentation of our results. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that HTT 'is shown to learn transferable representations that adapt to new tasks and previously unseen sensors' is asserted without any quantitative metrics, baselines, ablation studies, or error analysis. This absence makes it impossible to evaluate whether the generalization result holds or is affected by post-hoc experimental choices.

    Authors: We agree that the abstract, as a concise summary, would be strengthened by including key quantitative metrics to support the central claim. The full manuscript already contains the requested quantitative results, baselines, ablation studies, and error analyses in the experimental sections. To directly address this point, we will revise the abstract to incorporate specific performance numbers on transfer to new tasks and unseen sensors while retaining its brevity. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes a standard self-supervised pretraining pipeline (per-modality masked reconstruction + cross-modal alignment) on a new paired dataset (HPT), followed by empirical evaluation on downstream tasks. No equations, uniqueness theorems, or self-citations are presented that reduce claimed generalization performance to quantities defined by construction from the same fitted parameters or inputs. The central claim is an empirical demonstration of transfer, not a derivation that collapses to its own training objective.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that standard transformer training dynamics plus the described pretraining objectives suffice for cross-sensor generalization.

pith-pipeline@v0.9.1-grok · 5700 in / 1168 out tokens · 28652 ms · 2026-06-30T05:50:51.108571+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

45 extracted references · 17 canonical work pages

  1. [1]

    W. Yuan, S. Dong, and E. H. Adelson. Gelsight: High-resolution robot tactile sensors for estimating geometry and force.Sensors (Basel, Switzerland), 17, 2017. URLhttps://api. semanticscholar.org/CorpusID:3474913

    2017

  2. [2]

    Lambeta, P.-W

    M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V . R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer, D. Jayaraman, and R. Calandra. Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation. IEEE Robotics and Automation Letters, 5(3):3838–3845, July 2020. ISSN 2377-3774. doi:10.1109/lr...

    work page doi:10.1109/lra.2020.2977257 2020

  3. [3]

    C. Lin, H. Zhang, J. Xu, L. Wu, and H. Xu. 9dtact: A compact vision-based tactile sensor for accurate 3d shape reconstruction and generalizable 6d force estimation.IEEE Robotics and Automation Letters, 9(2):923–930, 2023

    2023

  4. [4]

    T. Tomo, M. Regoli, A. Schmitz, L. Natale, H. Kristanto, S. Somlor, L. Jamone, G. Metta, and S. Sugano. A new silicone structure for uskin - a soft, distributed, digital 3-axis skin sensor and its integration on the humanoid robot icub.IEEE Robotics and Automation Letters, PP: 1–1, 03 2018. doi:10.1109/LRA.2018.2812915

    work page doi:10.1109/lra.2018.2812915 2018

  5. [5]

    Bhirangi, T

    R. Bhirangi, T. Hellebrekers, C. Majidi, and A. Gupta. Reskin:versatile, replaceable, lasting tactile skins. InCoRL, 2021

    2021

  6. [6]

    TAC-02 — Robotic Finger Dev Kit.https://www.tacniq.ai/ tac-02-robotic-finger-dev-kit, n.d

    TacnIQ.ai. TAC-02 — Robotic Finger Dev Kit.https://www.tacniq.ai/ tac-02-robotic-finger-dev-kit, n.d. Accessed: 2025-12-10

    2025

  7. [7]

    Huang, Y

    B. Huang, Y . Wang, X. Yang, Y . Luo, and Y . Li. 3d vitac:learning fine-grained manipulation with visuo-tactile sensing. InProceedings of Robotics: Conference on Robot Learning(CoRL), 2024

    2024

  8. [8]

    Higuera, A

    C. Higuera, A. Sharma, C. K. Bodduluri, T. Fan, P. Lancaster, M. Kalakrishnan, M. Kaess, B. Boots, M. Lambeta, T. Wu, and M. Mukadam. Sparsh: Self-supervised touch representations for vision-based tactile sensing. In8th Annual Conference on Robot Learning,

  9. [9]

    URLhttps://openreview.net/forum?id=xYJn2e1uu8

  10. [10]

    J. Zhao, Y . Ma, L. Wang, and E. Adelson. Transferable tactile transformers for representation learning across diverse sensors and tasks. In8th Annual Conference on Robot Learning, 2024. URLhttps://openreview.net/forum?id=KXsropnmNI

    2024

  11. [11]

    Gupta, Y

    H. Gupta, Y . Mo, S. Jin, and W. Yuan. Sensor-invariant tactile representation. InThe Thirteenth International Conference on Learning Representations, 2025

    2025

  12. [12]

    R. Feng, J. Hu, W. Xia, A. Shen, Y . Sun, B. Fang, D. Hu, et al. Anytouch: Learning unified static-dynamic representation across multiple visuo-tactile sensors. InThe Thirteenth International Conference on Learning Representations

  13. [13]

    F. Yang, C. Feng, Z. Chen, H. Park, D. Wang, Y . Dou, Z. Zeng, X. Chen, R. Gangopadhyay, A. Owens, et al. Binding touch to everything: Learning unified multimodal tactile representations. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26340–26353, 2024

    2024

  14. [14]

    K. He, X. Chen, S. Xie, Y . Li, P. Doll ´ar, and R. Girshick. Masked autoencoders are scalable vision learners. In2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15979–15988, 2022. doi:10.1109/CVPR52688.2022.01553. 9

    work page doi:10.1109/cvpr52688.2022.01553 2022

  15. [15]

    C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song. Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots. InProceedings of Robotics: Science and Systems (RSS), 2024

    2024

  16. [16]

    S. Yu, L. Kelvin, and H. Soh. Demonstrating the Octopi-1.5 Visual-Tactile-Language Model. InProceedings of Robotics: Science and Systems, LosAngeles, CA, USA, June 2025. doi:10. 15607/RSS.2025.XXI.058

    2025

  17. [17]

    Q. Li, O. Kroemer, Z. Su, F. F. Veiga, M. Kaboli, and H. J. Ritter. A review of tactile information: Perception and action through touch.IEEE Transactions on Robotics, 36(6): 1619–1634, 2020

    2020

  18. [18]

    S. Luo, J. Bimbo, R. Dahiya, and H. Liu. Robotic tactile perception of object properties: A review.Mechatronics, 48:54–67, 2017

    2017

  19. [19]

    T. Li, Y . Yan, C. Yu, J. An, Y . Wang, and G. Chen. A comprehensive review of robot intelligent grasping based on tactile perception.Robotics and Computer-Integrated Manufacturing, 90: 102792, 2024

    2024

  20. [20]

    GelSight Mini Tactile Sensor.https://www.gelsight.com/ gelsightmini/, 2023

    GelSight Inc. GelSight Mini Tactile Sensor.https://www.gelsight.com/ gelsightmini/, 2023. Accessed: 2025-04-29

    2023

  21. [21]

    Lambeta, P.-W

    M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V . R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer, D. Jayaraman, and R. Calandra. Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation. IEEE Robotics and Automation Letters, 5(3):3838–3845, 2020. doi:10.1109/LRA.2020. 2977257

    work page doi:10.1109/lra.2020 2020

  22. [22]

    Lambeta, T

    M. Lambeta, T. Wu, A. Sengul, V . R. Most, N. Black, K. Sawyer, R. Mercado, H. Qi, A. Sohn, B. Taylor, N. Tydingco, G. Kammerer, D. Stroud, J. Khatha, K. Jenkins, K. Most, N. Stein, R. Chavira, T. Craven-Bartle, E. Sanchez, Y . Ding, J. Malik, and R. Calandra. Digitizing touch with an artificial multimodal fingertip, 2024. URLhttps://arxiv.org/abs/2411.02479

    work page arXiv 2024

  23. [23]

    J. Xu, W. Chen, H. Qian, D. Wu, and R. Chen. Thintact: Thin vision-based tactile sensor by lensless imaging.IEEE Transactions on Robotics, 2025

    2025

  24. [24]

    J. Zhao, N. Kuppuswamy, S. Feng, B. Burchfiel, and E. Adelson. Polytouch: A robust multi-modal tactile sensor for contact-rich manipulation using tactile-diffusion policies, 2025. URLhttps://arxiv.org/abs/2504.19341

    work page arXiv 2025

  25. [25]

    Khamis, R

    H. Khamis, R. Albero, M. Salerno, A. Shah Idil, and A. Loizou. Papillarray: An incipient slip sensor for dexterous robotic or prosthetic manipulation – design and prototype validation. Sensors and Actuators A: Physical, 270, 12 2017. doi:10.1016/j.sna.2017.12.058

    work page doi:10.1016/j.sna.2017.12.058 2017

  26. [26]

    F. Yang, C. Ma, J. Zhang, J. Zhu, W. Yuan, and A. Owens. Touch and go: Learning from human-collected vision and touch.arXiv preprint arXiv:2211.12498, 2022

    work page arXiv 2022

  27. [27]

    Suresh, Z

    S. Suresh, Z. Si, S. Anderson, M. Kaess, and M. Mukadam. MidasTouch: Monte-Carlo inference over distributions across sliding touch. InProc. Conf. on Robot Learning, CoRL, Auckland, NZ, Dec. 2022

    2022

  28. [28]

    S. Yu, K. Lin, A. Xiao, J. Duan, and H. Soh. Octopi: Object property reasoning with large tactile-language models. InProceedings of Robotics: Science and Systems, 2024

    2024

  29. [29]

    R. Feng, Y . Zhou, S. Mei, D. Zhou, P. Wang, S. Cui, B. Fang, G. Yao, and D. Hu. Anytouch 2: General optical tactile representation learning for dynamic tactile perception. InThe Fourteenth International Conference on Learning Representations, 2026. 10

    2026

  30. [30]

    Z. Si, G. Zhang, Q. Ben, B. Romero, Z. Xian, C. Liu, and C. Gan. DIFFTACTILE: A physics-based differentiable tactile simulator for contact-rich robotic manipulation. In The Twelfth International Conference on Learning Representations, 2024. URLhttps: //openreview.net/forum?id=eJHnSg783t

    2024

  31. [31]

    R. Gao, Z. Si, Y .-Y . Chang, S. Clarke, J. Bohg, L. Fei-Fei, W. Yuan, and J. Wu. Objectfolder 2.0: A multisensory object dataset for sim2real transfer. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10598–10608, 2022

    2022

  32. [32]

    Z. Xu, R. Uppuluri, X. Zhang, C. Fitch, P. G. Crandall, W. Shou, D. Wang, and Y . She. Unit: Data efficient tactile representation with generalization to unseen objects.IEEE Robotics and Automation Letters, 2025

    2025

  33. [33]

    J. Kerr, H. Huang, A. Wilcox, R. Hoque, J. Ichnowski, R. Calandra, and K. Goldberg. Self-supervised visuo-tactile pretraining to locate and follow garment features.arXiv preprint arXiv:2209.13042, 2022

    work page arXiv 2022

  34. [34]

    L. Fu, G. Datta, H. Huang, W. C.-H. Panitch, J. Drake, J. Ortiz, M. Mukadam, M. Lambeta, R. Calandra, and K. Goldberg. A touch, vision, and language dataset for multimodal alignment. InForty-first International Conference on Machine Learning, 2024. URLhttps: //openreview.net/forum?id=tFEOOH9eH0

    2024

  35. [35]

    Jones, O

    J. Jones, O. Mees, C. Sferrazza, K. Stachowicz, P. Abbeel, and S. Levine. Beyond sight: Finetuning generalist robot policies with heterogeneous sensors via language grounding, 2025. URLhttps://arxiv.org/abs/2501.04693

    work page arXiv 2025

  36. [36]

    Higuera, A

    C. Higuera, A. Sharma, T. Fan, C. K. Bodduluri, B. Boots, M. Kaess, M. Lambeta, T. Wu, Z. Liu, F. R. Hogan, et al. Tactile beyond pixels: Multisensory touch representations for robot manipulation.arXiv preprint arXiv:2506.14754, 2025

    work page arXiv 2025

  37. [37]

    J. Hou, X. Zhou, Q. Yang, and A. J. Spiers. Unitac-nv: A unified tactile representation for non-vision-based tactile sensors.arXiv preprint arXiv:2506.19699, 2025

    work page arXiv 2025

  38. [38]

    R. Gao, T. Taunyazov, Z. Lin, and Y . Wu. Supervised autoencoder joint learning on heterogeneous tactile sensory data: Improving material classification performance. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10907–10913. IEEE, 2020

    2020

  39. [39]

    Zandonati, R

    B. Zandonati, R. Wang, R. Gao, and Y . Wu. Investigating vision foundational models for tactile representation learning.ArXiv, abs/2305.00596, 2023. URLhttps://api. semanticscholar.org/CorpusID:258426712

    work page arXiv 2023

  40. [40]

    Grella, A

    F. Grella, A. Albini, G. Cannata, and P. Maiolino. Touch-to-touch translation-learning the mapping between heterogeneous tactile sensing technologies. In2025 IEEE 21st International Conference on Automation Science and Engineering (CASE), pages 1998–2004. IEEE, 2025

    1998

  41. [41]

    Z. Chen, F. Ni, K. Luo, Z. Wu, X. Zhang, E. Spyrakos-Papastavridis, L. Jamone, N. F. Lepora, J. Deng, and S. Luo. Uniforce: A unified latent force model for robot manipulation with diverse tactile sensors, 2026. URLhttps://arxiv.org/abs/2602.01153

    work page arXiv 2026

  42. [42]

    E. S. PAGE. Continuous inspection schemes.Biometrika, 41(1-2):100–115, 06 1954. ISSN 0006-3444. doi:10.1093/biomet/41.1-2.100. URLhttps://doi.org/10.1093/biomet/ 41.1-2.100

    work page doi:10.1093/biomet/41.1-2.100 1954

  43. [43]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. InInternational Conference on Learning Representations (ICLR), 2021. 11

    2021

  44. [44]

    T. Z. Zhao, V . Kumar, S. Levine, and C. Finn. Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. InProceedings of Robotics: Science and Systems, Daegu, Republic of Korea, July 2023. doi:10.15607/RSS.2023.XIX.016

    work page doi:10.15607/rss.2023.xix.016 2023

  45. [45]

    Q. K. Luu, P. Zhou, Z. Xu, Z. Zhang, Q. Qiu, and Y . She. Manifeel: Benchmarking and understanding visuotactile manipulation policy learning.arXiv preprint arXiv:2505.18472, 2025. 12 A Appendix Figure 4: The four heterogeneous tactile sensors used in HPT pretraining, each mounted in the UMI gripper shell. Left to right: GelSight Mini and 9DTact (optical-b...

    work page arXiv 2025