微型神经形态触觉传感器实现毫秒级精准触印追踪，适合高动态机器人操作。
SpikingTac: A Miniaturized Neuromorphic Visuotactile Sensor for High-Precision Dynamic Tactile Imprint Tracking
- 自研事件相机+无监督去噪，实现1000Hz感知与350Hz追踪
- 零点稳定率达100%，动态避障误差仅6.2mm，精度达亚毫米级
- 专为硅胶材料迟滞设计更新机制，适合精密抓取与力控场景
高速事件驱动触觉传感器对实现类人动态操作至关重要，但传统事件相机体积庞大限制集成。本文提出SpikingTac，一款成本低于150美元的微型化、高度集成神经形态触觉传感器，内置定制独立事件相机模块。通过构建全局动态状态图并结合无监督去噪网络，实现1000~Hz感知率与350~Hz追踪频率。针对硅胶弹性体的粘弹性迟滞问题，提出考虑迟滞的增量更新律与空间增益衰减机制。实验表明，该传感器具备优异零点稳定性，在极端扭转变形下仍实现100%归位成功率，均值偏差仅为0.8039像素。在动态任务中，障碍物避让超调量控制在6.2~mm，性能较传统帧基传感器提升5倍。定位与半径测量的均方根误差（RMSE）分别达0.0952 mm和0.0452 mm，实现亚毫米级几何精度。
原文摘要 · Abstract (English)
High-speed event-driven tactile sensors are essential for achieving human-like dynamic manipulation, yet their integration is often limited by the bulkiness of standard event cameras. This paper presents SpikingTac, a miniaturized, highly integrated neuromorphic tactile sensor featuring a custom standalone event camera module, achieved with a total material cost of less than \$150. We construct a global dynamic state map coupled with an unsupervised denoising network to enable precise tracking at a 1000~Hz perception rate and 350~Hz tracking frequency. Addressing the viscoelastic hysteresis of silicone elastomers, we propose a hysteresis-aware incremental update law with a spatial gain damping mechanism. Experimental results demonstrate exceptional zero-point stability, achieving a 100\% return-to-origin success rate with a minimal mean bias of 0.8039 pixels, even under extreme torsional deformations. In dynamic tasks, SpikingTac limits the obstacle-avoidance overshoot to 6.2~mm, representing a 5-fold performance improvement over conventional frame-based sensors. Furthermore, the sensor achieves sub-millimeter geometric accuracy, with Root Mean Square Error (RMSE) of 0.0952~mm in localization and 0.0452~mm in radius measurement.
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