用声音检测机器人是否按指令执行，无需改硬件。
WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
- 通过分析机器人运动时的声音信号验证动作
- 单个动作识别准确率超80%，全流程验证也可靠
- 适合安全要求高、不能改设备的工业场景
本文提出一种基于声学侧信道分析（ASCA）的框架，用于监控和验证机器人是否正确执行预定指令。我们构建并评估了一个基于机器学习的工作流验证系统，利用机器人运动产生的声学信号判断实时行为是否与预期命令一致。评估考虑了运动速度、方向及麦克风距离的影响。结果表明，在基准条件下，使用四种不同分类器（支持向量机、深度神经网络、循环神经网络、卷积神经网络）对单个机器人动作的验证准确率超过80%。此外，如抓取-放置和包装等复杂工作流也可实现高置信度验证。研究证明，声学信号可在不改变硬件的前提下，为敏感机器人环境提供实时、低成本、被动的验证手段。
原文摘要 · Abstract (English)
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 verified 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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