Finger-level tactile intelligence toward robotic dexterous operation via afferent encoding and neuronal multiplexing

Finger-level tactile intelligence toward robotic dexterous operation via afferent encoding and neuronal multiplexing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Finger-level tactile intelligence toward robotic dexterous operation via afferent encoding and neuronal multiplexing Xinge Yu, Jin Ma, Mengqi Fang, Mengli Sui, Jiayi Ding, Xinshi Yang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8222318/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Robotic dexterous operation necessitates a tactile sensing system that possesses capabilities equivalent to those of the human finger, but this benchmark remains unfulfilled by current sensors. Here, we establish a frequency-separated afferent encoding and neuronal multiplexing mechanism that fundamentally breaks from the conventional image-processing paradigm of visuotactile sensors. This mechanism enables, for the first time, human finger-level tactile intelligence by seamlessly integrating the recognition of contact force, surface roughness, and slip, which is implemented in a sensor named HydroPalm. HydroPalm achieves force perception with remarkable accuracy (R² = 0.965), producing force heatmaps strikingly similar to a human finger. Moreover, it demonstrates finger-level acuity in resolving micro-scale textures with a roughness average (Ra) of 0.258 μm, far surpassing existing tactile sensors. Its millisecond-level latency (< 75 ms) allows predictive detection of incipient slip, facilitating human-like reflexive grip adjustments. The pioneering finger-level tactile perception endows the underwater robotic pipe assembly with force perception, texture recognition, and slip prediction. This establishes a foundation for integrating human-level tactile intelligence into robotic dexterous manipulation. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Information technology Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.pdf Supplementary information Supplementaryvideo3.mp4 Supplementary video 3 Supplementaryvideo1.mp4 Supplementary video 1 Supplementaryvideo2.mp4 Supplementary video 2 Supplementaryvideo4.mp4 Supplementary video 4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8222318","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":579132754,"identity":"d349f8e9-a4d3-4ab3-87ce-b7e88591da2c","order_by":0,"name":"Xinge 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