# Uncertainty-driven Planner for Exploration and Navigation

arXiv: https://arxiv.org/abs/2202.11907 · HTML: https://mlsanity.com/paper/2202.11907/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 54 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Minimax Regret Bounds for Reinforcement Learning](https://mlsanity.com/paper/1703.05449/) (2017)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Self-Supervised Exploration via Disagreement](https://mlsanity.com/paper/1906.04161/) (2019)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [Learning Exploration Policies for Navigation](https://mlsanity.com/paper/1903.01959/) (2019)
- [Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks](https://mlsanity.com/paper/1903.03878/) (2019)
- [Occupancy Anticipation for Efficient Exploration and Navigation](https://mlsanity.com/paper/2008.09285/) (2020)
- [Visual Representations for Semantic Target Driven Navigation](https://mlsanity.com/paper/1805.06066/) (2018)
- [MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments](https://mlsanity.com/paper/1712.03931/) (2017)
- [Learning to Map for Active Semantic Goal Navigation](https://mlsanity.com/paper/2106.15648/) (2021)
- [SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation](https://mlsanity.com/paper/2012.04512/) (2020)
- [Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation](https://mlsanity.com/paper/2007.09841/) (2020)
- [Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation](https://mlsanity.com/paper/2105.07593/) (2021)
- [Learning to plan with uncertain topological maps](https://mlsanity.com/paper/2007.05270/) (2020)
- [The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation](https://mlsanity.com/paper/2108.11550/) (2021)

## Built on by (most cited first, 30 of 33)

- [Cross-modal Map Learning for Vision and Language Navigation](https://mlsanity.com/paper/2203.05137/) (2022)
- [RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation](https://mlsanity.com/paper/2402.15487/) (2024)
- [ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting](https://mlsanity.com/paper/2410.21955/) (2024)
- [Active Neural Mapping](https://mlsanity.com/paper/2308.16246/) (2023)
- [NARUTO: Neural Active Reconstruction from Uncertain Target Observations](https://mlsanity.com/paper/2402.18771/) (2024)
- [Neural Visibility Field for Uncertainty-Driven Active Mapping](https://mlsanity.com/paper/2406.06948/) (2024)
- [Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation](https://mlsanity.com/paper/2308.10306/) (2023)
- [ActiveGAMER: Active GAussian Mapping through Efficient Rendering](https://mlsanity.com/paper/2501.06897/) (2025)
- [NextBestPath: Efficient 3D Mapping of Unseen Environments](https://mlsanity.com/paper/2502.05378/) (2025)
- [Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization](https://mlsanity.com/paper/2310.06984/) (2023)
- [MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions](https://mlsanity.com/paper/2409.15590/) (2024)
- [MOPA: Modular Object Navigation with PointGoal Agents](https://mlsanity.com/paper/2304.03696/) (2023)
- [GLEAM: Learning Generalizable Exploration Policy for Active Mapping in Complex 3D Indoor Scenes](https://mlsanity.com/paper/2505.20294/) (2025)
- [Multimodal LLM Guided Exploration and Active Mapping using Fisher Information](https://mlsanity.com/paper/2410.17422/) (2024)
- [Active Neural Mapping at Scale](https://mlsanity.com/paper/2409.20276/) (2024)
- [Accelerating Residual Reinforcement Learning with Uncertainty Estimation](https://mlsanity.com/paper/2506.17564/) (2025)
- [Learning-Augmented Model-Based Planning for Visual Exploration](https://mlsanity.com/paper/2211.07898/) (2022)
- [Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data](https://mlsanity.com/paper/2407.08726/) (2024)
- [Understanding while Exploring: Semantics-driven Active Mapping](https://mlsanity.com/paper/2506.00225/) (2025)
- [Peering into the Unknown: Active View Selection with Neural Uncertainty Maps for 3D Reconstruction](https://mlsanity.com/paper/2506.14856/) (2025)
- [MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping](https://mlsanity.com/paper/2603.22650/) (2026)
- [GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraints](https://mlsanity.com/paper/2402.14354/) (2024)
- [Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation](https://mlsanity.com/paper/2212.08801/) (2022)
- [LatentBKI: Open-Dictionary Continuous Mapping in Visual-Language Latent Spaces with Quantifiable Uncertainty](https://mlsanity.com/paper/2410.11783/) (2024)
- [MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning](https://mlsanity.com/paper/2503.01548/) (2025)
- [HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward](https://mlsanity.com/paper/2510.15679/) (2025)
- [OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown Environments](https://mlsanity.com/paper/2406.07294/) (2024)
- [SenseExpo: Spatial Exploration and Navigation via Scene Estimation from Expeditious Predictive Operators](https://mlsanity.com/paper/2503.16000/) (2025)
- [Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration](https://mlsanity.com/paper/2504.04061/) (2025)
- [SuperEx: Enhancing Indoor Mapping and Exploration using Non-Line-of-Sight Perception](https://mlsanity.com/paper/2510.10506/) (2025)

## Abstract (arXiv)

We consider the problems of exploration and point-goal navigation in previously unseen environments, where the spatial complexity of indoor scenes and partial observability constitute these tasks challenging. We argue that learning occupancy priors over indoor maps provides significant advantages towards addressing these problems. To this end, we present a novel planning framework that first learns to generate occupancy maps beyond the field-of-view of the agent, and second leverages the model uncertainty over the generated areas to formulate path selection policies for each task of interest. For point-goal navigation the policy chooses paths with an upper confidence bound policy for efficient and traversable paths, while for exploration the policy maximizes model uncertainty over candidate paths. We perform experiments in the visually realistic environments of Matterport3D using the Habitat simulator and demonstrate: 1) Improved results on exploration and map quality metrics over competitive methods, and 2) The effectiveness of our planning module when paired with the state-of-the-art DD-PPO method for the point-goal navigation task.