WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows

• URL: http://arxiv.org/abs/2510.25960v1
• Date: Wed, 29 Oct 2025 20:58:16 GMT
• Title: WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows
• Abstract summary: We develop and evaluate a machine-learning-based workflow verification system.<n>The system can determine whether real-time behavior is consistent with expected commands.<n>Our findings demonstrate that acoustic signals can support real-time, low-cost, passive verification in sensitive robotic environments.
• Score: 1.7446020608840058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network [0.30786914102688595]
  This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm.<n>The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN)<n>The system exhibits stable real-time behavior, with an average latency of approximately 0.32 s consistent with the chosen windowing strategy.
  arXiv  Detail & Related papers  (2026-07-14T15:34:28Z)
• RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation [68.7948300643741]
  We propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention.<n>RC-NF decouples the processing of task-aware robot and object states within the normalizing flow.<n>It achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks.
  arXiv  Detail & Related papers  (2026-03-11T10:14:37Z)
• Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery [64.82869118243723]
  We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement.<n>Motivated by the promise of BCI-driven robotics, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner.
  arXiv  Detail & Related papers  (2026-03-03T17:41:42Z)
• A Segmented Robot Grasping Perception Neural Network for Edge AI [0.051776141577794685]
  This work implements Heatmap-Guided Grasp Detection on the GAP9 RISC-V System-on-Chip.<n>The model is optimised using hardware-aware techniques, including input dimensionality reduction, model partitioning, and quantisation.<n> Experimental evaluation on the GraspNet-1Billion benchmark validates the feasibility of fully on-chip inference.
  arXiv  Detail & Related papers  (2025-07-18T14:32:45Z)
• Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison [1.5817271400571666]
  This paper introduces a dual-layer framework for network operator-side quality of experience (QoE) assessment.<n>On the objective side, we develop a machine learning model trained on mean opinion scores (MOS) computed via the ITU-T P.1203 reference implementation.<n>On the subjective side, we present a semantic filtering and scoring pipeline that processes user comments from live streams to extract performance-related feedback.
  arXiv  Detail & Related papers  (2025-06-01T09:31:55Z)
• Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee [1.7456666582626115]
  This study implements a two-stage pipeline for gesture recognition through keypoint extraction and classification, alongside continuous convolutional neural networks (CCNNs) for efficient whistle detection. The proposed approach enhances real-time human-robot interaction in a competitive setting like RoboCup, offering some tools to advance the development of autonomous systems capable of cooperating with humans.
  arXiv  Detail & Related papers  (2024-11-26T11:39:43Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [72.81786007015471]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem.<n>CNN processes localized perception; a graph neural network (GNN) facilitates robot communications.<n> Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)
• Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation [0.0]
  We propose a novel low-complexity lidar gesture recognition system for mobile robot control. The system is lightweight and suitable for mobile robot control with limited computing power. The use of lidar contributes to the robustness of the system, allowing it to operate in most outdoor conditions.
  arXiv  Detail & Related papers  (2021-09-17T00:49:31Z)
• Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks [2.9439848714137447]
  We propose a neuromorphic approach to perform vibration analysis using spiking neural networks. We present a spike-based end-to-end pipeline able to detect system anomalies from vibration data. We show that the proposed method achieves state-of-the-art performance or better against two publicly available data sets.
  arXiv  Detail & Related papers  (2021-06-01T18:00:02Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)
• Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances [53.063441357826484]
  Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions. Speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances.
  arXiv  Detail & Related papers  (2020-02-14T13:34:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.