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<p>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 mm<sup>2</sup>) 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.</p>

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A bio-inspired origami capacitive robotic e-skin with multimodal sensing capabilities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Qian Xu,
  • Boyang Zhang,
  • Yik Kin Cheung,
  • Zhiwei Yang,
  • Rui Jiao,
  • Shuhuai Yao,
  • Wei Hong,
  • Hongyu Yu

摘要

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.