Machine Learning-Enhanced Modular Ionic Skin for Broad-Spectrum Multimodal Discriminability in Bidirectional Human–Robot Interaction (Adv. Mater. 42/2025)

IF 26.8 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Materials Pub Date : 2025-10-23 DOI:10.1002/adma.70838

Qianqian Yang, Bingqiao Li, Mengke Wang, Gaoyang Pang, Yuyao Lu, Jiayan Li, Huayong Yang, Honghao Lyu, Kaichen Xu, Geng Yang

引用次数: 0

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.

Abstract Image

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基于机器学习的模块化离子皮肤在双向人机交互中的广谱多模态识别（Adv. Mater. 42/2025）

数据驱动的解耦多模态离子皮肤在他们的研究文章(DOI: 10.1002/adma。202508795)，杨耿，徐凯晨等人报告了一种机器学习增强的模块化离子皮肤，能够进行大范围多模态解耦传感。离子凝胶的硬段调制和数据驱动的解耦模型等协同传感器算法优化策略使其具有出色的性能。通过操作者手识别和机器人传感反馈的功能验证强调了其在高级人机交互方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advanced Materials 工程技术-材料科学：综合

CiteScore

43.00

自引率

4.10%

发文量

2182

审稿时长

2 months

期刊介绍： Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.