# Bayesian Learning for the Robust Verification of Autonomous Robots

arXiv: https://arxiv.org/abs/2303.08476 · HTML: https://mlsanity.com/paper/2303.08476/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 72 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [A storm is Coming: A Modern Probabilistic Model Checker](https://mlsanity.com/paper/1702.04311/) (2017)
- [Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases](https://mlsanity.com/paper/1703.06350/) (2017)
- [Probabilistic Model Checking of Robots Deployed in Extreme Environments](https://mlsanity.com/paper/1812.04128/) (2018)
- [Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles](https://mlsanity.com/paper/2008.09510/) (2020)
- [Probabilistic Model Checking and Autonomy](https://mlsanity.com/paper/2111.10630/) (2021)
- [Bayesian Nonparametric System Reliability using Sets of Priors](https://mlsanity.com/paper/1602.01650/) (2016)
- [Marimba: A Tool for Verifying Properties of Hidden Markov Models](https://mlsanity.com/paper/1507.05597/) (2015)
- [New Opportunities for Integrated Formal Methods](https://mlsanity.com/paper/1812.10103/) (2018)

## Built on by (most cited first, 6 of 6)

- [A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models](https://mlsanity.com/paper/2511.00527/) (2025)
- [Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores](https://mlsanity.com/paper/2512.01080/) (2025)
- [Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs](https://mlsanity.com/paper/2604.21092/) (2026)
- [Exploring the Link Between Bayesian Inference and Embodied Intelligence: Toward Open Physical-World Embodied AI Systems](https://mlsanity.com/paper/2507.21589/) (2025)
- [Uncertainty-Aware Measurement of Scenario Suite Representativeness for Autonomous Systems](https://mlsanity.com/paper/2511.14853/) (2025)
- [Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification](https://mlsanity.com/paper/2608.03489/) (2026)

## Abstract (arXiv)

Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty.