Abstract
As embodied intelligence emerges, flexible electronics are attracting attention in wearable technology, healthcare, robotics, and human-machine interfaces. Electronic skins (e-skins) are vital for safe, efficient interaction, yet the structural and wiring complexity of conventional sensor arrays hinders scalability. Inspired by fish skin, we propose an origami-with-scale-based capacitive electronic skin that covers a large area (60000 mm2) and enables super-resolution tactile sensing by harnessing origami’s deformation transmission. Interdigital electrodes provide shear-force sensing, while a proximity-sensing layer detects approaching conductive objects, providing collision protection for humans. Additionally, machine learning algorithms are employed to enhance sensing accuracy, achieving a super-resolution (SR) factor of 241 with average localization and force magnitude estimation error of less than 3.5 mm and 0.04 N, respectively. By integrating theoretical models and machine learning algorithms, multi-point touch for non-adjacent loads was also realized. This design delivers a compact, multifunctional solution for large-area, super-resolution tactile sensing, advancing safe, immersive human-machine interaction and embodied intelligence.
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Data availability
The authors declare that all data needed to evaluate the conclusions are available in the article and the Supplementary Information. All data are available from the corresponding authors upon request. All code and data used for machine learning in this work are available in https://github.com/XuqianUST/Multimodal-E-skin.
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Acknowledgements
This study was funded by the Research Grants Council of Hong Kong under the General Research Fund (16204124), and the Innovation and Technology Commission (project: GHP/021/22) of HKSAR.Q.X.’s visit to Southern University of Science and Technology was supported by the SUSTech Fellow program.All the funders played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript.
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H.Y., W.H. and S.Y. conceived and supervised the project. Q.X. designed, fabricated, and tested the E-skins. Q.X. and Z.Y. carried out the theoretical analysis and FEA; Build the data collection platform. Q.X., B.Z. and Y.C. developed machine learning algorithms and trained the model. Q.X. and B.Z. conduct the demonstrations. R.J. and Y.C. helped with article writing. All authors contributed to the discussion of the results.
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Xu, Q., Zhang, B., Cheung, Y.K. et al. A bio-inspired origami capacitive robotic e-skin with multimodal sensing capabilities. npj Flex Electron (2026). https://doi.org/10.1038/s41528-026-00563-3
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DOI: https://doi.org/10.1038/s41528-026-00563-3
