An Intrinsically Multimodal Self-Powered Sensor Enhanced by Microstructured Powder Layer for AI-Enabled Tactile Perception

Abstract

Artificial intelligence (AI)-powered robots increasingly rely on advanced tactile sensors to perceive and interpret complex mechanical cues, enabling intelligent interaction with real-world environments. However, most existing tactile sensing systems rely on different sensing mechanisms to achieve static and dynamic perception, which increases system complexity. In this work, we present the self-powered intrinsic Tactile-Dual mode (iTD) Sensor—an intrinsically multimodal triboelectric platform that integrates material recognition and dual-mode (static/dynamic) pressure sensing within a single sensor device. A microstructured polytetrafluoroethylene powder layer, introduced via scalable spray-coating, endows the sensor with high sensing resolution and strong moisture resistance. The iTD Sensor intrinsically decouples static and dynamic signals without auxiliary circuitry, allowing for efficient and complementary tactile data acquisition. Leveraging these signals, a convolutional neural network model achieves material classification with 99.08% accuracy. For pressure sensing, the iTD Sensor exhibits high sensitivities across static (< 3 kPa, 7.62 V kPa⁻¹; 3–30 kPa, 0.59 V kPa⁻¹) and dynamic (< 5 kPa, 5.56 V kPa⁻¹; 5–30 kPa, 0.30 V kPa⁻¹) regimes. Integrated onto a robotic fingertip, the sensor enables accurate recognition of real-world objects and surface textures, achieving classification accuracies of 98.75% and 99.38%, respectively. This work provides a compact, scalable, and AI-compatible tactile sensing solution for intelligent robots operating in complex environments.

Keywords

AI-enhanced material recognition / intrinsic static/dynamic sensor / robotic perception / self-powered tactile sensing

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2026 The Author(s). Interdisciplinary Materials published by Wuhan University of Technology and John Wiley & Sons Australia, Ltd.