# Learning to plan with uncertain topological maps

arXiv: https://arxiv.org/abs/2007.05270 · HTML: https://mlsanity.com/paper/2007.05270/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 29 of 29)

- [A Survey of Embodied AI: From Simulators to Research Tasks](https://mlsanity.com/paper/2103.04918/) (2021)
- [Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2210.07506/) (2022)
- [Memory-Augmented Reinforcement Learning for Image-Goal Navigation](https://mlsanity.com/paper/2101.05181/) (2021)
- [No RL, No Simulation: Learning to Navigate without Navigating](https://mlsanity.com/paper/2110.09470/) (2021)
- [Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.01176/) (2021)
- [Uncertainty-driven Planner for Exploration and Navigation](https://mlsanity.com/paper/2202.11907/) (2022)
- [Prior Does Matter: Visual Navigation via Denoising Diffusion Bridge Models](https://mlsanity.com/paper/2504.10041/) (2025)
- [FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation](https://mlsanity.com/paper/2310.07473/) (2023)
- [Multi-Object Navigation with dynamically learned neural implicit representations](https://mlsanity.com/paper/2210.05129/) (2022)
- [Learning Active Camera for Multi-Object Navigation](https://mlsanity.com/paper/2210.07505/) (2022)
- [Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding](https://mlsanity.com/paper/2303.04077/) (2023)
- [Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation](https://mlsanity.com/paper/2107.06011/) (2021)
- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results](https://mlsanity.com/paper/2212.06759/) (2022)
- [MemoNav: Working Memory Model for Visual Navigation](https://mlsanity.com/paper/2402.19161/) (2024)
- [GraphMapper: Efficient Visual Navigation by Scene Graph Generation](https://mlsanity.com/paper/2205.08325/) (2022)
- [Graph augmented Deep Reinforcement Learning in the GameRLand3D environment](https://mlsanity.com/paper/2112.11731/) (2021)
- [Learning to navigate efficiently and precisely in real environments](https://mlsanity.com/paper/2401.14349/) (2024)
- [TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments](https://mlsanity.com/paper/2402.04061/) (2024)
- [Godot Reinforcement Learning Agents](https://mlsanity.com/paper/2112.03636/) (2021)
- [RANa: Retrieval-Augmented Navigation](https://mlsanity.com/paper/2504.03524/) (2025)
- [What does really matter in image goal navigation?](https://mlsanity.com/paper/2507.01667/) (2025)
- [Kinaema: a recurrent sequence model for memory and pose in motion](https://mlsanity.com/paper/2510.20261/) (2025)
- [MemoNav: Selecting Informative Memories for Visual Navigation](https://mlsanity.com/paper/2208.09610/) (2022)
- [Active Neural Topological Mapping for Multi-Agent Exploration](https://mlsanity.com/paper/2311.00252/) (2023)
- [VSGM -- Enhance robot task understanding ability through visual semantic graph](https://mlsanity.com/paper/2105.08959/) (2021)
- [Predicting Topological Maps for Visual Navigation in Unexplored Environments](https://mlsanity.com/paper/2211.12649/) (2022)
- [FeudalNav: A Simple Framework for Visual Navigation](https://mlsanity.com/paper/2602.06974/) (2026)
- [Fisher-Preserving Guidance: Training-Free Manifold Constraints for Safe Diffusion Control](https://mlsanity.com/paper/2605.29937/) (2026)

## Abstract (arXiv)

We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure. Whereas classical symbolic algorithms achieve optimal results on noise-less topologies, or optimal results in a probabilistic sense on graphs with probabilistic structure, we aim to show that machine learning can overcome missing information in the graph by taking into account rich high-dimensional node features, for instance visual information available at each location of the map. Compared to purely learned neural white box algorithms, we structure our neural model with an inductive bias for dynamic programming based shortest path algorithms, and we show that a particular parameterization of our neural model corresponds to the Bellman-Ford algorithm. By performing an empirical analysis of our method in simulated photo-realistic 3D environments, we demonstrate that the inclusion of visual features in the learned neural planner outperforms classical symbolic solutions for graph based planning.