# Learning Dynamic Cognitive Map with Autonomous Navigation

arXiv: https://arxiv.org/abs/2411.08447 · HTML: https://mlsanity.com/paper/2411.08447/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 56 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 27 of 27)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [The StreetLearn Environment and Dataset](https://mlsanity.com/paper/1903.01292/) (2019)
- [Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models](https://mlsanity.com/paper/1810.11692/) (2018)
- [Rapid Task-Solving in Novel Environments](https://mlsanity.com/paper/2006.03662/) (2020)
- [Bayesian model reduction](https://mlsanity.com/paper/1805.07092/) (2018)
- [Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results](https://mlsanity.com/paper/2212.06759/) (2022)
- [Demystification of Few-shot and One-shot Learning](https://mlsanity.com/paper/2104.12174/) (2021)
- [Autonomous Navigation of AGVs in Unknown Cluttered Environments: log-MPPI Control Strategy](https://mlsanity.com/paper/2203.16599/) (2022)
- [Supervised structure learning](https://mlsanity.com/paper/2311.10300/) (2023)
- [Graph schemas as abstractions for transfer learning, inference, and planning](https://mlsanity.com/paper/2302.07350/) (2023)
- [Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment](https://mlsanity.com/paper/2312.05058/) (2023)
- [Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus](https://mlsanity.com/paper/2212.01508/) (2022)
- [Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments](https://mlsanity.com/paper/2401.05946/) (2024)
- [Towards Efficient MPPI Trajectory Generation with Unscented Guidance: U-MPPI Control Strategy](https://mlsanity.com/paper/2306.12369/) (2023)
- [Integrating cognitive map learning and active inference for planning in ambiguous environments](https://mlsanity.com/paper/2308.08307/) (2023)
- [Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building](https://mlsanity.com/paper/2307.05793/) (2023)
- [A Brain-Inspired Compact Cognitive Mapping System](https://mlsanity.com/paper/1910.03913/) (2019)

## Built on by (most cited first, 3 of 3)

- [Video2Layout: Recall and Reconstruct Metric-Grounded Cognitive Map for Spatial Reasoning](https://mlsanity.com/paper/2511.16160/) (2025)
- [Thinking on Maps: How Foundation Model Agents Explore, Remember, and Reason Map Environments](https://mlsanity.com/paper/2512.24504/) (2025)
- [Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences](https://mlsanity.com/paper/2607.12382/) (2026)

## Abstract (arXiv)

Inspired by animal navigation strategies, we introduce a novel computational model to navigate and map a space rooted in biologically inspired principles. Animals exhibit extraordinary navigation prowess, harnessing memory, imagination, and strategic decision-making to traverse complex and aliased environments adeptly. Our model aims to replicate these capabilities by incorporating a dynamically expanding cognitive map over predicted poses within an Active Inference framework, enhancing our agent's generative model plasticity to novelty and environmental changes. Through structure learning and active inference navigation, our model demonstrates efficient exploration and exploitation, dynamically expanding its model capacity in response to anticipated novel un-visited locations and updating the map given new evidence contradicting previous beliefs. Comparative analyses in mini-grid environments with the Clone-Structured Cognitive Graph model (CSCG), which shares similar objectives, highlight our model's ability to rapidly learn environmental structures within a single episode, with minimal navigation overlap. Our model achieves this without prior knowledge of observation and world dimensions, underscoring its robustness and efficacy in navigating intricate environments.