WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows

TL;DR

This paper introduces WaveVerif, a novel acoustic side-channel analysis framework that employs machine learning to verify robotic workflows in real-time, achieving over 80% accuracy without hardware modifications.

Contribution

WaveVerif is the first system to use acoustic emissions and machine learning for real-time verification of robotic commands and workflows.

Findings

Over 80% accuracy in validating individual robot movements.

High-confidence identification of complex workflows like pick-and-place.

Acoustic signals enable passive, low-cost verification without hardware changes.

Abstract

In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior is consistent with expected commands. The evaluation takes into account movement speed, direction, and microphone distance. The results show that individual robot movements can be validated with over 80% accuracy under baseline conditions using four different classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Additionally, workflows such as pick-and-place and packing could be identified with similarly high confidence. Our findings demonstrate that acoustic signals can support real-time, low-cost, passive verification in sensitive robotic environments without requiring hardware modifications.