Sandwich Miura‐Ori Enabled Large Area, Super Resolution Tactile Skin for Human–Machine Interactions

This project, backed by research, looks at how to design and support a sandwich‑Miura‑ori (SMo) tactile skin that can sense over large areas with super‑resolution. Conventional tactile sensor arrays become hard to wire and expensive to make when they are scaled up. To solve these problems, the approach uses an origami‑inspired SMo structure that can detect both normal and shear forces while using only a few sensing units. A detailed theoretical model captures the physical variations that occur during manufacturing, so the skin can be calibrated for many different shapes. Machine‑learning algorithms take the sparse sensor data and turn it into high‑resolution touch locations and accurate force readings. In tests,

the system achieved an average position error of 1.89 mm and an average force error of 8 %. The design also includes a curved SMo skin that has been optimized with a tessellation algorithm for mounting on robotic arms. Overall, the work provides a step‑by‑step guide for simulating, calibrating, and evaluating super‑resolution tactile skins, offering a cost‑effective and scalable option for advanced robotic feedback and human‑machine interfaces.

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