WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
This provides a low-cost, passive verification method for sensitive robotic environments, though it is incremental as it applies existing machine learning techniques to a new application.
The paper tackles the problem of verifying robotic workflows by using acoustic side-channel analysis to monitor if a robot correctly executes commands, achieving over 80% accuracy in validating individual movements and high confidence in identifying workflows like pick-and-place.
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.