Multi-Touch and Bending Sensing Using Electrical Impedance Tomography for Robotics

arXiv:2503.13048v4 Announce Type: replace Abstract: Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in robotic applications. However, EIT-based tactile sensors face significant challenges during surface bending. Deformation alters the baseline impedance distribution and couples with touch-induced conductivity variations, complicating signal interpretation. To address this challenge, we present a novel sensing framework that integrates a deep neural network for interaction state classification with a dynamic adaptive reference strategy to decouple touch and deformation signals, while a data-driven regression model translates EIT voltage changes into continuous bending angles. The framework is validated using a magnetic hydrogel composite sensor that conforms to bendable surfaces. Experimental evaluations demonstrate that the proposed framework achieves precise and robust bending angle estimation, high accuracy in distinguishing touch, bending, and idle states, and significantly improves touch localization quality under bending deformation compared to conventional fixed-reference methods. Real-time experiments confirm the system's capability to reliably detect multi-touch interactions and track bending angles across varying deformation conditions. This work paves the way for flexible EIT-based robotic skins capable of rich multimodal sensing in robotics and human-robot interaction.

Abstract:Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in robotic applications. However, EIT-based tactile sensors face significant challenges during surface bending. Deformation alters the baseline impedance distribution and couples with touch-induced conductivity variations, complicating signal interpretation. To address this challenge, we present a novel sensing framework that integrates a deep neural network for interaction state classification with a dynamic adaptive reference strategy to decouple touch and deformation signals, while a data-driven regression model translates EIT voltage changes into continuous bending angles. The framework is validated using a magnetic hydrogel composite sensor that conforms to bendable surfaces. Experimental evaluations demonstrate that the proposed framework achieves precise and robust bending angle estimation, high accuracy in distinguishing touch, bending, and idle states, and significantly improves touch localization quality under bending deformation compared to conventional fixed-reference methods. Real-time experiments confirm the system's capability to reliably detect multi-touch interactions and track bending angles across varying deformation conditions. This work paves the way for flexible EIT-based robotic skins capable of rich multimodal sensing in robotics and human-robot interaction.

Robotics (cs.RO)

arXiv:2503.13048 [cs.RO]

(or arXiv:2503.13048v4 [cs.RO] for this version)

Chen, H.; Himmel, B.; Li, B.; Wang, X. & Hoffmann, M. (2026), 'Multi-Touch and Bending Sensing Using Electrical Impedance Tomography for Robotics', IEEE Robotics and Automation Letters 11(8), 9851-9858

https://doi.org/10.1109/LRA.2026.3709063 DOI(s) linking to related resources

From: Matej Hoffmann Ph.D. [view email] [v1] Mon, 17 Mar 2025 10:53:42 UTC (4,638 KB) [v2] Tue, 26 Aug 2025 06:43:58 UTC (656 KB) [v3] Tue, 2 Sep 2025 15:47:35 UTC (656 KB) [v4] Wed, 26 Aug 2026 16:13:28 UTC (586 KB)

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Multi-Touch and Bending Sensing Using Electrical Impedance Tomography for Robotics

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