Stretchable e-skin and transformer enable high-resolution morphological reconstruction for soft robots

Delin Hu, Francesco Giorgio-Serchi, Shiming Zhang, Yunjie Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Many robotic tasks require knowledge of the exact 3D robot geometry. However, this remains extremely challenging in soft robotics because of the infinite degrees of freedom of soft bodies deriving from their continuum characteristics. Previous studies have only achieved low proprioceptive geometry resolution (PGR), thus suffering from loss of geometric details (e.g. local deformation and surface information) and limited applicability. Here, we report an intelligent stretchable capacitive e-skin to endow soft robots with high PGR (=3,900) bodily awareness. We demonstrate that the proposed e-skin can finely capture a wide
range of complex 3D deformations across the entire soft body through multi-position capacitance measurements. The e-skin signals can be directly translated to high-density point clouds portraying the complete geometry via a deep architecture based on transformer. This high PGR proprioception system providing millimeter-scale, local and global geometry reconstruction (2.322±0.687 mm error on a 20×20×200 mm soft manipulator) can assist in solving fundamental problems in soft robotics, such as precise closed-loop control and digital twin modelling.
Original languageEnglish
JournalNature Machine Intelligence
Early online date23 Feb 2023
Publication statusE-pub ahead of print - 23 Feb 2023


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