# Previous Knowledge Utilization In Online Anytime Belief Space Planning

arXiv: https://arxiv.org/abs/2412.13128 · HTML: https://mlsanity.com/paper/2412.13128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [Optimality Guarantees for Particle Belief Approximation of POMDPs](https://mlsanity.com/paper/2210.05015/) (2022)
- [HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty](https://mlsanity.com/paper/1802.06215/) (2018)
- [An On-Line POMDP Solver for Continuous Observation Spaces](https://mlsanity.com/paper/2011.02076/) (2020)
- [iX-BSP: Incremental Belief Space Planning](https://mlsanity.com/paper/2102.09539/) (2021)
- [Adaptive Information Belief Space Planning](https://mlsanity.com/paper/2201.05673/) (2022)
- [Monte Carlo Information-Oriented Planning](https://mlsanity.com/paper/2103.11345/) (2021)
- [No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification](https://mlsanity.com/paper/2310.10274/) (2023)

## Built on by (most cited first, 2 of 2)

- [Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs](https://mlsanity.com/paper/2503.12181/) (2025)
- [REBA: A Revealed Belief Automaton Framework for Online Planning in Continuous POMDPs](https://mlsanity.com/paper/2606.21971/) (2026)

## Abstract (arXiv)

Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future trajectories within computational constraints, most existing methods discard information from previous planning sessions considering continuous spaces. This study presents a novel, computationally efficient approach that leverages historical planning data in current decision-making processes. We provide theoretical foundations for our information reuse strategy and introduce an algorithm based on Monte Carlo Tree Search (MCTS) that implements this approach. Experimental results demonstrate that our method significantly reduces computation time while maintaining high performance levels. Our findings suggest that integrating historical planning information can substantially improve the efficiency of online decision-making in uncertain environments, paving the way for more responsive and adaptive autonomous systems.