# Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.05997 · HTML: https://mlsanity.com/paper/2506.05997/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 49 of its 80 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 30 of 49)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [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)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers](https://mlsanity.com/paper/2107.03996/) (2021)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators](https://mlsanity.com/paper/2406.20083/) (2024)
- [Advanced Skills by Learning Locomotion and Local Navigation End-to-End](https://mlsanity.com/paper/2209.12827/) (2022)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots](https://mlsanity.com/paper/1609.07910/) (2016)
- [Elevation Mapping for Locomotion and Navigation using GPU](https://mlsanity.com/paper/2204.12876/) (2022)

## Built on by (most cited first, 11 of 11)

- [Vision-Language Memory for Spatial Reasoning](https://mlsanity.com/paper/2511.20644/) (2025)
- [DeFM: Learning Foundation Representations from Depth for Robotics](https://mlsanity.com/paper/2601.18923/) (2026)
- [OpenFrontier: General Navigation with Visual-Language Grounded Frontiers](https://mlsanity.com/paper/2603.05377/) (2026)
- [RAVEN: Long-Horizon Reasoning & Navigation with a Visuo-Spatio-Temporal Memory](https://mlsanity.com/paper/2606.25206/) (2026)
- [Navigating Simply, Aligning Deeply: Winning Solutions for Mouse vs. AI 2025](https://mlsanity.com/paper/2602.00982/) (2026)
- [Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation](https://mlsanity.com/paper/2603.13888/) (2026)
- [GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation](https://mlsanity.com/paper/2606.10832/) (2026)
- [NavCMPO: Critic-Guided MeanFlow Policy Optimization for Adaptive Navigation](https://mlsanity.com/paper/2607.14643/) (2026)
- [Predictive Training with Latent Imagination for Visual Quadruped Navigation](https://mlsanity.com/paper/2607.17574/) (2026)
- [Visual Navigation Transformer with Pose Attention](https://mlsanity.com/paper/2609.21212/) (2026)
- [GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation](https://mlsanity.com/paper/2610.17927/) (2026)

## Abstract (arXiv)

Recent advancements in robot navigation, particularly with end-to-end learning approaches such as reinforcement learning (RL), have demonstrated strong performance. However, successful navigation still depends on two key capabilities: mapping and planning (explicitly or implicitly). Classical approaches rely on explicit mapping pipelines to register egocentric observations into a coherent map. In contrast, end-to-end learning often achieves this implicitly -- through recurrent neural networks (RNNs) that fuse current and historical observations into a latent space for planning. While existing architectures, such as LSTM and GRU, can capture temporal dependencies, our findings reveal a critical limitation: their inability to effectively perform spatial memorization. This capability is essential for integrating sequential observations from varying perspectives to build spatial representations that support planning. To address this, we propose Spatially-Enhanced Recurrent Units (SRUs) -- a simple yet effective modification to existing RNNs -- that enhance spatial memorization. We further introduce an attention-based network architecture integrated with SRUs, enabling long-range mapless navigation using a single forward-facing stereo camera. We also employ regularization techniques to facilitate robust end-to-end recurrent training via RL. Experimental results show 23.5% overall improvement in long-range navigation compared to existing RNNs. With SRU memory, our method outperforms RL baselines -- one relying on explicit mapping and the other on stacked historical observations -- by 29.6% and 105.0%, respectively, across diverse environments requiring long-horizon mapping and memorization. Finally, we address the sim-to-real gap by leveraging large-scale pretraining on synthetic depth data, enabling zero-shot transfer for deployment across diverse and complex real-world environments.