Machine Learning‐Enhanced Modular Ionic Skin for Broad‐Spectrum Multimodal Discriminability in Bidirectional Human–Robot Interaction (Adv. Mater. 42/2025)
Source abstract
Abstract
Data-Driven Decoupled Multimodal Ionic Skin In their Research Article (DOI: 10.1002/adma.202508795), Geng Yang, Kaichen Xu, and co-workers report a machine learning-enhanced modular ionic skin capable of large-range multimodal decoupled sensing. Its outstanding performance is enabled by a synergistic sensor-algorithm optimization strategy, including hard-segment modulation of ionic gels and data-driven decoupling model. Functional validation via operator hand recognition and robotic sensing feedback underscore its potential for advanced human-robot interaction.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials
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Affiliations
State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou 310000 China
Polytechnic Institute Zhejiang University Hangzhou 310000 China
School of Electrical and Computer Engineering The University of Sydney Sydney 2006 Australia
Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou 310000 China
References
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