mlsanityData to 14 Oct 2026

A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder

arXiv 1711.00614 ↗First posted 2 Nov 2017cs.RO cs.LGcited by 209

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Abstract (arXiv)

The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem. We introduce a long short-term memory based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution. We also introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score and a state-based threshold. For evaluations with 1,555 robot-assisted feeding executions including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve (AUC) of 0.8710 than 5 other baseline detectors from the literature. We also show the multimodal fusion through the LSTM-VAE is effective by comparing our detector with 17 raw sensory signals versus 4 hand-engineered features.

Key facts. Cited by 209 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Nov 2017.

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Citing papers per year: 2021: 29; 2022: 37; 2023: 39; 2024: 33; 2025: 32; 2026: 39.

209 papers.

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  1. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  2. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  3. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  4. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  5. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  6. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  7. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  8. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  9. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  10. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  11. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  12. 2022A survey of multimodal deep generative modelscited by 47
38 more
  1. 2021Neural Contextual Anomaly Detection for Time Seriescited by 43
  2. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 40
  3. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 38
  4. 2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
  5. 2024Large language models can be zero-shot anomaly detectors for time series?cited by 31
  6. 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
  7. 2023MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detectioncited by 26
  8. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  9. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 25
  10. 2023AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Schemecited by 25
  11. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  12. 2021DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detectioncited by 21
  13. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  14. 2021Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturingcited by 19
  15. 2022Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?cited by 19
  16. 2022LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Datacited by 19
  17. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 18
  18. 2021Co-GAIL: Learning Diverse Strategies for Human-Robot Collaborationcited by 17
  19. 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
  20. 2023When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detectioncited by 17
  21. 2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?cited by 17
  22. 2022Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detectioncited by 16
  23. 2024VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robotscited by 16
  24. 2021Anomaly Detection in Multi-Agent Trajectories for Automated Drivingcited by 15
  25. 2022TiSAT: Time Series Anomaly Transformercited by 15
  26. 2022Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusioncited by 15
  27. 2023CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detectioncited by 15
  28. 2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
  29. 2021Federated Variational Learning for Anomaly Detection in Multivariate Time Seriescited by 12
  30. 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
  31. 2023Robotic Applications of Pre-Trained Vision-Language Models to Various Recognition Behaviorscited by 12
  32. 2024PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detectioncited by 12
  33. 2025GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causalitycited by 12
  34. 2025Harnessing Vision-Language Models for Time Series Anomaly Detectioncited by 11
  35. 2022Time-Series Anomaly Detection with Implicit Neural Representationcited by 10
  36. 2023OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecastingcited by 10
  37. 2023DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detectioncited by 10
  38. 2024Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Valuescited by 10

50 most cited of 209 All 209 (JSON)