Compressed Sensing for Scalable Robotic Tactile Skins

TL;DR

This paper introduces a compressed sensing approach for tactile arrays in robotics, enabling high-quality data reconstruction and object classification with significantly reduced measurements, thus addressing hardware and data processing challenges.

Contribution

It applies compressed sensing to tactile data acquisition and classification, achieving efficient data compression and high accuracy in robotic tactile sensing.

Findings

Reconstructed full tactile signals from 1024 measurements out of 4096 with high fidelity.

Achieved up to 98% object classification accuracy at a 64:1 compression ratio.

Operates at over 100Hz, enabling real-time tactile data processing.

Abstract

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable human-level dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, data acquisition, and data processing. To help address these challenges, we develop a tactile sensing technique based on compressed sensing. Compressed sensing simultaneously performs data sampling and compression with recovery guarantees and has been successfully applied in computer vision. We use compressed sensing techniques for tactile data acquisition to reduce hardware complexity and data transmission, while allowing fast, accurate reconstruction of the full-resolution signal. For our simulated test array of 4096 taxels, we achieve reconstruction quality equivalent to measuring all taxel signals independently (the full signal) from just 1024 measurements (the compressed signal) at a rate over 100Hz. We then apply tactile compressed sensing to the problem of object classification. Specifically, we perform object classification on the compressed tactile data based on a method called compressed learning. We obtain up to 98% classification accuracy, even with a compression ratio of 64:1.