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

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.

Figures (15)

• Figure 1: We present SpikingTac, a miniaturized neuromorphic visuotactile tactile sensor designed for seamless integration into dexterous robotic hands. By leveraging dynamic state map reconstruction for tactile imprints, SpikingTac enables high-bandwidth dynamic perception and ensures robust marker tracking under rapid physical interactions.
• Figure 2: Design of SpikingTac. (a) The exploded view of SpikingTac. The components labeled as 1 are screws for connecting the sensor base, the shading plate and the sensor shell. The components labeled as 6 are heat-set threaded inserts. The components labeled as 10 are 64 white circular markers arranged in an 8x8 grid. (b) The schematic diagram of SpikingTac.
• Figure 3: Comparison image of Gelsight Mini dotted gel and SpikingTac dotted gel.
• Figure 4: Overview of the proposed event-driven marker tracking pipeline. The process begins with Event Stream Capture, followed by three core phases: Geometric Reconstruction (orange), which builds and denoises the state map; Robust Calibration (grey), which aggregates multi-directional motion to determine initial reference positions; and Dynamic Tracking (green), which implements a hysteresis-aware update law with a spatial damping operator to ensure zero-baseline stability.
• Figure 5: Conceptual illustration of the event-driven marker reconstruction. Complete shape reconstruction: As the white circular marker translates by a distance equal to its diameter, the asynchronous event stream (represented by $p=+1$ and $p=-1$ polarities) cumulatively populates and clears the state map $M(x,y)$, eventually reconstructing the full geometry at the terminal position.