# Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling

arXiv: https://arxiv.org/abs/2411.07032 · HTML: https://mlsanity.com/paper/2411.07032/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [Vectorized Online POMDP Planning](https://mlsanity.com/paper/2510.27191/) (2025)
- [Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing](https://mlsanity.com/paper/2508.12166/) (2025)
- [Partially Observable Reference Policy Programming: Solving POMDPs Sans Numerical Optimisation](https://mlsanity.com/paper/2507.12186/) (2025)
- [Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving](https://mlsanity.com/paper/2602.08334/) (2026)

## Abstract (arXiv)

Partially Observable Markov Decision Processes (POMDPs) are a general and principled framework for motion planning under uncertainty. Despite tremendous improvement in the scalability of POMDP solvers, long-horizon POMDPs (e.g., ≥15 steps) remain difficult to solve. This paper proposes a new approximate online POMDP solver, called Reference-Based Online POMDP Planning via Rapid State Space Sampling (ROP-RaS3). ROP-RaS3 uses novel extremely fast sampling-based motion planning techniques to sample the state space and generate a diverse set of macro actions online which are then used to bias belief-space sampling and infer high-quality policies without requiring exhaustive enumeration of the action space -- a fundamental constraint for modern online POMDP solvers. ROP-RaS3 is evaluated on various long-horizon POMDPs, including on a problem with a planning horizon of more than 100 steps and a problem with a 15-dimensional state space that requires more than 20 look ahead steps. In all of these problems, ROP-RaS3 substantially outperforms other state-of-the-art methods by up to multiple folds.