SpikingTac: A Miniaturized Neuromorphic Visuotactile Sensor for High-Precision Dynamic Tactile Imprint Tracking

TL;DR

SpikingTac is a compact, neuromorphic visuotactile sensor capable of high-precision, high-speed dynamic tactile tracking, outperforming traditional sensors in stability, accuracy, and obstacle avoidance.

Contribution

The paper introduces SpikingTac, a miniaturized neuromorphic tactile sensor with a custom event camera and advanced algorithms for precise, high-speed tactile perception and tracking.

Findings

Achieves 1000 Hz perception rate and 350 Hz tracking frequency.

Demonstrates zero-point stability with 100% return-to-origin success.

Limits obstacle-avoidance overshoot to 6.2 mm, five times better than conventional sensors.

Abstract

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.