# Adaptive Information Belief Space Planning

arXiv: https://arxiv.org/abs/2201.05673 · HTML: https://mlsanity.com/paper/2201.05673/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [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)
- [An On-Line POMDP Solver for Continuous Observation Spaces](https://mlsanity.com/paper/2011.02076/) (2020)
- [Information-Guided Robotic Maximum Seek-and-Sample in Partially Observable Continuous Environments](https://mlsanity.com/paper/1909.12216/) (2019)
- [Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency](https://mlsanity.com/paper/2105.14239/) (2021)
- [Monte Carlo Information-Oriented Planning](https://mlsanity.com/paper/2103.11345/) (2021)
- [Online POMDP Planning via Simplification](https://mlsanity.com/paper/2105.05296/) (2021)

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

- [Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints](https://mlsanity.com/paper/2302.06697/) (2023)
- [Risk Aware Adaptive Belief-dependent Probabilistically Constrained Continuous POMDP Planning](https://mlsanity.com/paper/2209.02679/) (2022)
- [Monte Carlo Planning in Hybrid Belief POMDPs](https://mlsanity.com/paper/2211.07735/) (2022)
- [Previous Knowledge Utilization In Online Anytime Belief Space Planning](https://mlsanity.com/paper/2412.13128/) (2024)
- [Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees](https://mlsanity.com/paper/2303.02139/) (2023)
- [Measurement Simplification in ρ-POMDP with Performance Guarantees](https://mlsanity.com/paper/2309.10701/) (2023)

## Abstract (arXiv)

Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms cannot either reason about uncertainty explicitly, or do so with a high computational burden. Here, we focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We formulate an approximation, namely an abstract observation model, that uses an aggregation scheme to alleviate computational costs. We derive bounds on the expected information-theoretic reward function and, as a consequence, on the value function. We then propose a method to refine aggregation to achieve identical action selection with a fraction of the computational time.