仿乳突结构的类脑触觉系统可提前360毫秒检测滑动前兆，提升机器人操作安全
A Neuromorphic Incipient Slip Detection System using Papillae Morphology
- 模仿乳突结构设计触觉皮肤，结合脉冲神经网络实现低功耗滑动分类
- 在动态重力滑动测试中，94.33%准确率识别三类滑动状态，提前360毫秒预警
- 适合边缘部署的类脑感知系统，特别适用于对能耗敏感的机器人应用
检测滑动前兆可实现早期干预，防止物体滑脱并提升机器人操作安全性。然而，在边缘平台部署此类系统仍具挑战，尤其受限于能量约束。本文提出一种基于NeuroTac传感器、具有凸起乳突结构皮肤的类脑触觉系统，并采用脉冲卷积神经网络（SCNN）进行滑动状态分类。在由传感器运动引发的滑动条件下，该SCNN模型在三类状态（无滑动、滑动前兆、严重滑动）上达到94.33%的分类准确率。在动态重力诱导的滑动验证条件下，经对SCNN最后一层脉冲计数进行时间平滑后，系统在所有试验中至少提前360毫秒检测到滑动前兆，且始终在严重滑动发生前识别出滑动前兆。结果表明，该类脑系统具备稳定且响应迅速的滑动前兆检测能力。
原文摘要 · Abstract (English)
Detecting incipient slip enables early intervention to prevent object slippage and enhance robotic manipulation safety. However, deploying such systems on edge platforms remains challenging, particularly due to energy constraints. This work presents a neuromorphic tactile sensing system based on the NeuroTac sensor with an extruding papillae-based skin and a spiking convolutional neural network (SCNN) for slip-state classification. The SCNN model achieves 94.33% classification accuracy across three classes (no slip, incipient slip, and gross slip) in slip conditions induced by sensor motion. Under the dynamic gravity-induced slip validation conditions, after temporal smoothing of the SCNN's final-layer spike counts, the system detects incipient slip at least 360 ms prior to gross slip across all trials, consistently identifying incipient slip before gross slip occurs. These results demonstrate that this neuromorphic system has stable and responsive incipient slip detection capability.
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