# WayFAST: Navigation with Predictive Traversability in the Field

arXiv: https://arxiv.org/abs/2203.12071 · HTML: https://mlsanity.com/paper/2203.12071/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks](https://mlsanity.com/paper/1609.05130/) (2016)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Learning Navigation Behaviors End-to-End with AutoRL](https://mlsanity.com/paper/1809.10124/) (2018)
- [RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation](https://mlsanity.com/paper/1806.01054/) (2018)
- [Combining Optimal Control and Learning for Visual Navigation in Novel Environments](https://mlsanity.com/paper/1903.02531/) (2019)
- [Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images](https://mlsanity.com/paper/2002.10570/) (2020)
- [Safe Robot Navigation via Multi-Modal Anomaly Detection](https://mlsanity.com/paper/2001.07934/) (2020)
- [Learned Visual Navigation for Under-Canopy Agricultural Robots](https://mlsanity.com/paper/2107.02792/) (2021)
- [Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees](https://mlsanity.com/paper/2102.09027/) (2021)
- [Nonlinear MPC for Collision Avoidance and Controlof UAVs With Dynamic Obstacles](https://mlsanity.com/paper/2008.00792/) (2020)

## Built on by (most cited first, 30 of 32)

- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving](https://mlsanity.com/paper/2402.19341/) (2024)
- [Last-Mile Embodied Visual Navigation](https://mlsanity.com/paper/2211.11746/) (2022)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision](https://mlsanity.com/paper/2404.07110/) (2024)
- [DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation](https://mlsanity.com/paper/2403.09900/) (2024)
- [Exploitation-Guided Exploration for Semantic Embodied Navigation](https://mlsanity.com/paper/2311.03357/) (2023)
- [Interactive Navigation in Environments with Traversable Obstacles Using Large Language and Vision-Language Models](https://mlsanity.com/paper/2310.08873/) (2023)
- [Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception](https://mlsanity.com/paper/2306.14601/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy](https://mlsanity.com/paper/2406.02989/) (2024)
- [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)
- [Unmatched uncertainty mitigation through neural network supported model predictive control](https://mlsanity.com/paper/2304.11315/) (2023)
- [Dynamics Modeling using Visual Terrain Features for High-Speed Autonomous Off-Road Driving](https://mlsanity.com/paper/2412.00581/) (2024)
- [Circular Accessible Depth: A Robust Traversability Representation for UGV Navigation](https://mlsanity.com/paper/2212.13676/) (2022)
- [Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation](https://mlsanity.com/paper/2404.17820/) (2024)
- [TE-NeXt: A LiDAR-Based 3D Sparse Convolutional Network for Traversability Estimation](https://mlsanity.com/paper/2406.01395/) (2024)
- [Learning Autonomy: Off-Road Navigation Enhanced by Human Input](https://mlsanity.com/paper/2502.18760/) (2025)
- [Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments](https://mlsanity.com/paper/2605.28442/) (2026)
- [Learning Agile Paths from Optimal Control](https://mlsanity.com/paper/2212.00184/) (2022)
- [METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation](https://mlsanity.com/paper/2307.13991/) (2023)
- [Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation](https://mlsanity.com/paper/2409.09921/) (2024)
- [Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation](https://mlsanity.com/paper/2509.11082/) (2025)
- [Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data](https://mlsanity.com/paper/2605.27644/) (2026)
- [GAFT: Geo-Anchored Fine-Tuning for Hazard Identification from Rare Failures](https://mlsanity.com/paper/2608.30858/) (2026)
- [UDAV: Uncertainty-Driven Adaptive VLM Waypoint Planner](https://mlsanity.com/paper/2609.16368/) (2026)
- [Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation](https://mlsanity.com/paper/2609.17141/) (2026)
- [FLINT: Fast Lightweight Inference for Traversability](https://mlsanity.com/paper/2609.26857/) (2026)

## Abstract (arXiv)

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.