# An On-Line POMDP Solver for Continuous Observation Spaces

arXiv: https://arxiv.org/abs/2011.02076 · HTML: https://mlsanity.com/paper/2011.02076/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [Optimality Guarantees for Particle Belief Approximation of POMDPs](https://mlsanity.com/paper/2210.05015/) (2022)
- [Bridging POMDPs and Bayesian decision making for robust maintenance planning under model uncertainty: An application to railway systems](https://mlsanity.com/paper/2212.07933/) (2022)
- [Compositional Learning-based Planning for Vision POMDPs](https://mlsanity.com/paper/2112.09456/) (2021)
- [Smoother Entropy for Active State Trajectory Estimation and Obfuscation in POMDPs](https://mlsanity.com/paper/2108.10227/) (2021)
- [Adaptive Information Belief Space Planning](https://mlsanity.com/paper/2201.05673/) (2022)
- [Adaptive Discretization using Voronoi Trees for Continuous POMDPs](https://mlsanity.com/paper/2302.10439/) (2023)
- [Survey of Computerized Adaptive Testing: A Machine Learning Perspective](https://mlsanity.com/paper/2404.00712/) (2024)
- [Online POMDP Planning with Anytime Deterministic Optimality Guarantees](https://mlsanity.com/paper/2310.01791/) (2023)
- [Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice](https://mlsanity.com/paper/2311.07745/) (2023)
- [Previous Knowledge Utilization In Online Anytime Belief Space Planning](https://mlsanity.com/paper/2412.13128/) (2024)
- [No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification](https://mlsanity.com/paper/2310.10274/) (2023)
- [Partially Observable Monte-Carlo Graph Search](https://mlsanity.com/paper/2507.20951/) (2025)
- [Perception-Based Beliefs for POMDPs with Visual Observations](https://mlsanity.com/paper/2602.05679/) (2026)
- [PlanScope: Learning to Plan Within Decision Scope for Urban Autonomous Driving](https://mlsanity.com/paper/2411.00476/) (2024)
- [REBA: A Revealed Belief Automaton Framework for Online Planning in Continuous POMDPs](https://mlsanity.com/paper/2606.21971/) (2026)

## Abstract (arXiv)

Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solvers in the past two decades. However, computing robust solutions for problems with continuous observation spaces remains challenging. Most on-line solvers rely on discretising the observation space or artificially limiting the number of observations that are considered during planning to compute tractable policies. In this paper we propose a new on-line POMDP solver, called Lazy Belief Extraction for Continuous POMDPs (LABECOP), that combines methods from Monte-Carlo-Tree-Search and particle filtering to construct a policy reprentation which doesn't require discretised observation spaces and avoids limiting the number of observations considered during planning. Experiments on three different problems involving continuous observation spaces indicate that LABECOP performs similar or better than state-of-the-art POMDP solvers.