Single-Pixel Tactile Skin via Compressive Sampling

TL;DR

This paper introduces Single-Pixel Tactile Skin (SPTS), a hardware-efficient system using compressive sampling to reconstruct detailed tactile information from large sensor arrays with minimal wiring and high-speed data acquisition.

Contribution

The paper presents a novel hardware implementation of compressive sensing for tactile skins, enabling simplified wiring, high-speed data capture, and adaptive reconstruction for robotics applications.

Findings

Achieved object classification at 3500 FPS

Captured projectile impact in 8 ms with 23 frames

Reduced data requirements to 7% for rapid localization

Abstract

Development of large-area, high-speed electronic skins is a grand challenge for robotics, prosthetics, and human-machine interfaces, but is fundamentally limited by wiring complexity and data bottlenecks. Here, we introduce Single-Pixel Tactile Skin (SPTS), a paradigm that uses compressive sampling to reconstruct rich tactile information from an entire sensor array via a single output channel. This is achieved through a direct circuit-level implementation where each sensing element, equipped with a miniature microcontroller, contributes a dynamically weighted analog signal to a global sum, performing distributed compressed sensing in hardware. Our flexible, daisy-chainable design simplifies wiring to a few input lines and one output, and significantly reduces measurement requirements compared to raster scanning methods. We demonstrate the system's performance by achieving object classification at an effective 3500 FPS and by capturing transient dynamics, resolving an 8 ms projectile impact into 23 frames. A key feature is the support for adaptive reconstruction, where sensing fidelity scales with measurement time. This allows for rapid contact localization using as little as 7% of total data, followed by progressive refinement to a high-fidelity image - a capability critical for responsive robotic systems. This work offers an efficient pathway towards large-scale tactile intelligence for robotics and human-machine interfaces.