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

arXiv: https://arxiv.org/abs/2101.01364 · HTML: https://mlsanity.com/paper/2101.01364/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2021.

## Built on by (most cited first, 26 of 26)

- [SAFE: Multitask Failure Detection for Vision-Language-Action Models](https://mlsanity.com/paper/2506.09937/) (2025)
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- [Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies](https://mlsanity.com/paper/2503.08558/) (2025)
- [Failure Prediction at Runtime for Generative Robot Policies](https://mlsanity.com/paper/2510.09459/) (2025)
- [A System-Level View on Out-of-Distribution Data in Robotics](https://mlsanity.com/paper/2212.14020/) (2022)
- [Unifying Evaluation of Machine Learning Safety Monitors](https://mlsanity.com/paper/2208.14660/) (2022)
- [FSNet: A Failure Detection Framework for Semantic Segmentation](https://mlsanity.com/paper/2108.08748/) (2021)
- [Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring](https://mlsanity.com/paper/2605.30834/) (2026)
- [Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification](https://mlsanity.com/paper/2205.10906/) (2022)
- [Evaluation of Runtime Monitoring for UAV Emergency Landing](https://mlsanity.com/paper/2202.03059/) (2022)
- [Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers](https://mlsanity.com/paper/2309.13475/) (2023)
- [LUNA: A Model-Based Universal Analysis Framework for Large Language Models](https://mlsanity.com/paper/2310.14211/) (2023)
- [System Safety Monitoring of Learned Components Using Temporal Metric Forecasting](https://mlsanity.com/paper/2405.13254/) (2024)
- [Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns](https://mlsanity.com/paper/2404.07685/) (2024)
- [Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving](https://mlsanity.com/paper/2501.08083/) (2025)
- [Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems](https://mlsanity.com/paper/2510.21254/) (2025)
- [Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey](https://mlsanity.com/paper/2511.05982/) (2025)
- [SAFECAST: Robust Failure Detection for VLA Policies with Contrast-Set Training and Calibration](https://mlsanity.com/paper/2608.04246/) (2026)
- [High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement](https://mlsanity.com/paper/2306.16132/) (2023)
- [Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework](https://mlsanity.com/paper/2307.04533/) (2023)
- [Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering](https://mlsanity.com/paper/2405.07600/) (2024)
- [A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System](https://mlsanity.com/paper/2412.16762/) (2024)
- [Edge Radar Material Classification Under Geometry Shifts](https://mlsanity.com/paper/2603.23342/) (2026)
- [LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Driving](https://mlsanity.com/paper/2606.00372/) (2026)
- [Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels](https://mlsanity.com/paper/2607.26121/) (2026)

## 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.