# Visual Memory for Robust Path Following

arXiv: https://arxiv.org/abs/1812.00940 · HTML: https://mlsanity.com/paper/1812.00940/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 16 of 16)

- [Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method](https://mlsanity.com/paper/2412.09082/) (2024)
- [Memory-Augmented Reinforcement Learning for Image-Goal Navigation](https://mlsanity.com/paper/2101.05181/) (2021)
- [ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints](https://mlsanity.com/paper/2202.11271/) (2022)
- [AffordanceLLM: Grounding Affordance from Vision Language Models](https://mlsanity.com/paper/2401.06341/) (2024)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [Phone2Proc: Bringing Robust Robots Into Our Chaotic World](https://mlsanity.com/paper/2212.04819/) (2022)
- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [ObjectReact: Learning Object-Relative Control for Visual Navigation](https://mlsanity.com/paper/2509.09594/) (2025)
- [Feudal Networks for Visual Navigation](https://mlsanity.com/paper/2402.12498/) (2024)
- [PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model](https://mlsanity.com/paper/2410.11564/) (2024)
- [Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation](https://mlsanity.com/paper/2212.08801/) (2022)
- [MemoNav: Selecting Informative Memories for Visual Navigation](https://mlsanity.com/paper/2208.09610/) (2022)
- [Semantic Environment Atlas for Object-Goal Navigation](https://mlsanity.com/paper/2410.09081/) (2024)
- [TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route](https://mlsanity.com/paper/2509.18173/) (2025)
- [FeudalNav: A Simple Framework for Visual Navigation](https://mlsanity.com/paper/2602.06974/) (2026)
- [Where Did It Go Wrong? Capability-Oriented Failure Attribution for Vision-and-Language Navigation Agents](https://mlsanity.com/paper/2604.25161/) (2026)

## Abstract (arXiv)

Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.