mlsanityData to 7 Oct 2026

Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends

arXiv 2101.01364 ↗First posted 5 Jan 2021cs.ROcited by 26

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Abstract (arXiv)

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is increasingly becoming an essential building block for robotic perception. This raises important questions concerning the safety and reliability of learning-based perception systems. There is an established field that studies safety certification and convergence guarantees of complex software systems at design-time. However, the unknown future deployment environments of an autonomous system and the complexity of learning-based perception make the generalization of design-time verification to run-time problematic. In the face of this challenge, more attention is starting to focus on run-time monitoring of performance and reliability of perception systems with several trends emerging in the literature. This paper attempts to identify these trends and summarise the various approaches to the topic.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 4; 2024: 6; 2025: 6; 2026: 5.

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  1. 2025SAFE: Multitask Failure Detection for Vision-Language-Action Modelscited by 74
  2. 2024Real-Time Anomaly Detection and Reactive Planning with Large Language Modelscited by 67
  3. 2024Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Drivingcited by 55
  4. 2025Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policiescited by 49
  5. 2025Failure Prediction at Runtime for Generative Robot Policiescited by 36
  6. 2022A System-Level View on Out-of-Distribution Data in Roboticscited by 33
  7. 2021FSNet: A Failure Detection Framework for Semantic Segmentationcited by 9
  8. 2022Unifying Evaluation of Machine Learning Safety Monitorscited by 8
  9. 2022Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identificationcited by 6
  10. 2026Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoringcited by 6
  11. 2022Evaluation of Runtime Monitoring for UAV Emergency Landingcited by 4
  12. 2023Detecting and Mitigating System-Level Anomalies of Vision-Based Controllerscited by 4
14 more
  1. 2023LUNA: A Model-Based Universal Analysis Framework for Large Language Modelscited by 4
  2. 2024System Safety Monitoring of Learned Components Using Temporal Metric Forecastingcited by 4
  3. 2024Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patternscited by 3
  4. 2025Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Drivingcited by 2
  5. 2025Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systemscited by 2
  6. 2025Runtime Safety Monitoring of Deep Neural Networks for Perception: A Surveycited by 2
  7. 2026SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibrationcited by 2
  8. 2023High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinementcited by 1
  9. 2023Preventing Errors in Person Detection: A Part-Based Self-Monitoring Frameworkcited by 0
  10. 2024Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filteringcited by 0
  11. 2024A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving Systemcited by 0
  12. 2026Edge Radar Material Classification Under Geometry Shiftscited by 0
  13. 2026LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Drivingcited by 0
  14. 2026Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levelscited by 0