# Safe POMDP Online Planning via Shielding

arXiv: https://arxiv.org/abs/2309.10216 · HTML: https://mlsanity.com/paper/2309.10216/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 19 Sep 2023.

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

- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Safe Reinforcement Learning via Shielding under Partial Observability](https://mlsanity.com/paper/2204.00755/) (2022)
- [Online Planning Algorithms for POMDPs](https://mlsanity.com/paper/1401.3436/) (2014)
- [Enforcing Almost-Sure Reachability in POMDPs](https://mlsanity.com/paper/2007.00085/) (2020)
- [Optimizing Expectation with Guarantees in POMDPs (Technical Report)](https://mlsanity.com/paper/1611.08696/) (2016)
- [Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives](https://mlsanity.com/paper/1804.10601/) (2018)
- [Planning for Automated Vehicles with Human Trust](https://mlsanity.com/paper/2101.03267/) (2021)

## Built on by (most cited first, 8 of 8)

- [Conformal Safety Shielding for Imperfect-Perception Agents](https://mlsanity.com/paper/2506.17275/) (2025)
- [Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments](https://mlsanity.com/paper/2409.18411/) (2024)
- [Adaptive Shielding for Safe Reinforcement Learning under Hidden-Parameter Dynamics Shifts](https://mlsanity.com/paper/2506.11033/) (2025)
- [Shields to Guarantee Probabilistic Safety in MDPs](https://mlsanity.com/paper/2605.10888/) (2026)
- [Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed](https://mlsanity.com/paper/2601.21094/) (2026)
- [Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning](https://mlsanity.com/paper/2605.25267/) (2026)
- [REBA: A Revealed Belief Automaton Framework for Online Planning in Continuous POMDPs](https://mlsanity.com/paper/2606.21971/) (2026)
- [Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding](https://mlsanity.com/paper/2609.07618/) (2026)

## Abstract (arXiv)

Partially observable Markov decision processes (POMDPs) have been widely used in many robotic applications for sequential decision-making under uncertainty. POMDP online planning algorithms such as Partially Observable Monte-Carlo Planning (POMCP) can solve very large POMDPs with the goal of maximizing the expected return. But the resulting policies cannot provide safety guarantees which are imperative for real-world safety-critical tasks (e.g., autonomous driving). In this work, we consider safety requirements represented as almost-sure reach-avoid specifications (i.e., the probability to reach a set of goal states is one and the probability to reach a set of unsafe states is zero). We compute shields that restrict unsafe actions which would violate the almost-sure reach-avoid specifications. We then integrate these shields into the POMCP algorithm for safe POMDP online planning. We propose four distinct shielding methods, differing in how the shields are computed and integrated, including factored variants designed to improve scalability. Experimental results on a set of benchmark domains demonstrate that the proposed shielding methods successfully guarantee safety (unlike the baseline POMCP without shielding) on large POMDPs, with negligible impact on the runtime for online planning.