# WayFASTER: a Self-Supervised Traversability Prediction for Increased Navigation Awareness

arXiv: https://arxiv.org/abs/2402.00683 · HTML: https://mlsanity.com/paper/2402.00683/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2024.

## Built on by (most cited first, 10 of 10)

- [PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain](https://mlsanity.com/paper/2409.03005/) (2024)
- [GeNIE: A Generalizable Navigation System for In-the-Wild Environments](https://mlsanity.com/paper/2506.17960/) (2025)
- [RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation](https://mlsanity.com/paper/2409.10940/) (2024)
- [Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation](https://mlsanity.com/paper/2411.04112/) (2024)
- [ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments](https://mlsanity.com/paper/2508.19131/) (2025)
- [Self-supervised cost of transport estimation for multimodal path planning](https://mlsanity.com/paper/2412.06101/) (2024)
- [Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments](https://mlsanity.com/paper/2605.28442/) (2026)
- [Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation](https://mlsanity.com/paper/2409.09921/) (2024)
- [FLINT: Fast Lightweight Inference for Traversability](https://mlsanity.com/paper/2609.26857/) (2026)
- [From Language Priors to Field Adaptation: Preference Learning for Traversability Estimation](https://mlsanity.com/paper/2610.02974/) (2026)

## Abstract (arXiv)

Accurate and robust navigation in unstructured environments requires fusing data from multiple sensors. Such fusion ensures that the robot is better aware of its surroundings, including areas of the environment that are not immediately visible but were visible at a different time. To solve this problem, we propose a method for traversability prediction in challenging outdoor environments using a sequence of RGB and depth images fused with pose estimations. Our method, termed WayFASTER (Waypoints-Free Autonomous System for Traversability with Enhanced Robustness), uses experience data recorded from a receding horizon estimator to train a self-supervised neural network for traversability prediction, eliminating the need for heuristics. Our experiments demonstrate that our method excels at avoiding obstacles, and correctly detects that traversable terrains, such as tall grass, can be navigable. By using a sequence of images, WayFASTER significantly enhances the robot's awareness of its surroundings, enabling it to predict the traversability of terrains that are not immediately visible. This enhanced awareness contributes to better navigation performance in environments where such predictive capabilities are essential.