Pneumatic-Tomographic Tactile Skin for Multicontact Localization and Force Estimation

[2503.13036] Pneumatic-Tomographic Tactile Skin for Multicontact Localization and Force Estimation Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2503.13036 (cs) [Submitted on 17 Mar 2025 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: Pneumatic-Tomographic Tactile Skin for Multicontact Localization and Force Estimation Authors: Haofeng Chen , Jiri Kubik , Bedrich Himmel , Matej Hoffmann , Hyosang Lee View PDF HTML (experimental) Abstract: Tactile skins based on electrical impedance tomography (EIT) enable large-area contact localization with few electrodes, but suffer from nonuniform sensitivity that limits force estimation accuracy. This work introduces a dual-channel tactile skin that integrates an EIT layer with a pneumatic pressure layer and a calibration framework that leverages their complementary strengths. The EIT layer provides robust multicontact localization, while the pneumatic pressure layer supplies a stable scalar measurement that serves as contact force estimation. A location-aware correction method is introduced, learning smooth spatial gain and offset fields from a single-session calibration, enabling spatially consistent multicontact force estimation. With location-aware correction, the proposed system achieves a single-contact force estimation root-mean-square error (RMSE) of 0.59 N across 10-25-mm indenters, representing a 35%-60% reduction over EIT-only approaches (1.45-1.48 N). In multicontact experiments, the per-contact RMSE is reduced by 39.6% compared to the uncorrected pneumatic baseline. The proposed system achieves accurate force estimation across diverse contact configurations, generalizes to varying indenter sizes, and preserves EIT's inherent advantages in multicontact localization. By letting the pneumatic pressure layer handle the force estimation and using the EIT layer to determine where each contact occurs, the method avoids the need for large datasets, complicated calibration setups, and heavy machine-learning pipelines often required by previous EIT-only approaches. This dual-channel design provides a practical, scalable, and easy-to-calibrate solution for building large-area robotic skins. Subjects: Robotics (cs.RO) ; Signal Processing (eess.SP) MSC classes: 93C85, 65N21, 94A08 ACM classes: I.2.9; I.4.5; I.4.8 Cite as: arXiv:2503.13036 [cs.RO] (or arXiv:2503.13036v3 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2503.13036 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Chen, H., Kubik, J., Himmel, B., Hoffmann, M. and Lee, H. (2026), 'Pneumatic-Tomographic Tactile Skin for Multicontact Localization and Force Estimation', IEEE Sensors 26(16), 23655 - 23665 Related DOI : https://doi.org/10.1109/JSEN.2026.3704916 Focus to learn more DOI(s) linking to related resources Submission history From: Matej Hoffmann Ph.D. [ view email ] [v1] Mon, 17 Mar 2025 10:41:13 UTC (1,451 KB) [v2] Thu, 18 Dec 2025 18:54:47 UTC (1,099 KB) [v3] Thu, 27 Aug 2026 08:55:40 UTC (1,312 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2025-03 Change to browse by: cs eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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