# Probabilistic Model Checking of Robots Deployed in Extreme Environments

arXiv: https://arxiv.org/abs/1812.04128 · HTML: https://mlsanity.com/paper/1812.04128/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2018.

## Built on by (most cited first, 12 of 12)

- [Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance](https://mlsanity.com/paper/2112.00646/) (2021)
- [Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles](https://mlsanity.com/paper/2106.01258/) (2021)
- [The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems](https://mlsanity.com/paper/2105.04408/) (2021)
- [Bayesian Learning for the Robust Verification of Autonomous Robots](https://mlsanity.com/paper/2303.08476/) (2023)
- [Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking](https://mlsanity.com/paper/2109.06523/) (2021)
- [Reinforcement Learning Under Probabilistic Spatio-Temporal Constraints with Time Windows](https://mlsanity.com/paper/2307.15910/) (2023)
- [A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models](https://mlsanity.com/paper/2511.00527/) (2025)
- [Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review](https://mlsanity.com/paper/2307.08024/) (2023)
- [Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review](https://mlsanity.com/paper/2505.07911/) (2025)
- [Accelerating Policy Synthesis in Large-Scale MDPs via Hierarchical Adaptive Refinement](https://mlsanity.com/paper/2506.17792/) (2025)
- [SCP-NL2TL: Selective Conformal Prediction with Semantic Verification for Natural Language to Temporal Logic Specifications](https://mlsanity.com/paper/2608.05439/) (2026)
- [Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification](https://mlsanity.com/paper/2608.03489/) (2026)

## Abstract (arXiv)

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot's safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.