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Learning‐Based Damage Recovery for Healable Soft Electronic Skins
Author(s) -
Terryn Seppe,
Hardman David,
Thuruthel Thomas George,
Roels Ellen,
Sahraeeazartamar Fatemeh,
Iida Fumiya
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202200115
Subject(s) - robustness (evolution) , computer science , damages , adaptation (eye) , artificial intelligence , materials science , biomedical engineering , engineering , neuroscience , political science , law , gene , biology , biochemistry , chemistry
Natural agents display various adaptation strategies to damages, including damage assessment, localization, healing, and recalibration. This work investigates strategies by which a soft electronic skin can similarly preserve its sensitivity after multiple damages, combining material‐level healing with software‐level adaptation. Being manufactured entirely from self‐healing Diels–Alder matrix and composite fibers, the skin is capable of physically recovering from macroscopic damages. However, the simultaneous shifts in sensor fiber signals cannot be modeled using analytical approaches because the materials viscoelasticity and healing processes introduce significant nonlinearities and time‐variance into the skin's response. It is shown that machine learning of five‐layer networks after 5000 probes leads to highly sensitive models for touch localization with 2.3 mm position and 95% depth accuracy. Through health monitoring via probing, damage and partial recovery are localized. Although healing is often successful, insufficient recontact leads to limited recovery or complete loss of a fiber. In these cases, complete resampling and retraining recovers the networks’ full performance, regaining sensitivity, and further increasing the system's robustness. Transfer learning with a single frozen layer provides the ability to rapidly adapt with fewer than 200 probes.

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