WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows

📝 Original Info

• Title: WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
• ArXiv ID: 2510.25960
• Date: 2025-10-29
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (예시: 홍길동, 김철수, 박영희 등) **

📝 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.

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