# Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision

arXiv: https://arxiv.org/abs/2404.07110 · HTML: https://mlsanity.com/paper/2404.07110/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [Elevation Mapping for Locomotion and Navigation using GPU](https://mlsanity.com/paper/2204.12876/) (2022)
- [Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning](https://mlsanity.com/paper/1912.03227/) (2019)
- [Safe Robot Navigation via Multi-Modal Anomaly Detection](https://mlsanity.com/paper/2001.07934/) (2020)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation](https://mlsanity.com/paper/2312.16016/) (2023)
- [Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments](https://mlsanity.com/paper/2203.15854/) (2022)
- [STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation](https://mlsanity.com/paper/2103.02828/) (2021)
- [Autonomous Exploration Development Environment and the Planning Algorithms](https://mlsanity.com/paper/2110.14573/) (2021)
- [How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability](https://mlsanity.com/paper/2209.10788/) (2022)
- [GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2209.05722/) (2022)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments](https://mlsanity.com/paper/2209.06522/) (2022)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Learning Ground Traversability from Simulations](https://mlsanity.com/paper/1709.05368/) (2017)
- [Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map](https://mlsanity.com/paper/2203.13429/) (2022)
- [Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion](https://mlsanity.com/paper/2204.01146/) (2022)
- [An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions](https://mlsanity.com/paper/2201.03938/) (2022)
- [MEM: Multi-Modal Elevation Mapping for Robotics and Learning](https://mlsanity.com/paper/2309.16818/) (2023)

## Built on by (most cited first, 17 of 17)

- [Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation](https://mlsanity.com/paper/2408.16567/) (2024)
- [VAMOS: A Hierarchical Vision-Language-Action Model for Capability-Modulated and Steerable Navigation](https://mlsanity.com/paper/2510.20818/) (2025)
- [Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features](https://mlsanity.com/paper/2501.17594/) (2025)
- [Let Humanoids Hike! Integrative Skill Development on Complex Trails](https://mlsanity.com/paper/2505.06218/) (2025)
- [Dynamics Modeling using Visual Terrain Features for High-Speed Autonomous Off-Road Driving](https://mlsanity.com/paper/2412.00581/) (2024)
- [Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation](https://mlsanity.com/paper/2511.19647/) (2025)
- [Scene-Agnostic Traversability Labeling and Estimation via a Multimodal Self-supervised Framework](https://mlsanity.com/paper/2508.18249/) (2025)
- [Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation](https://mlsanity.com/paper/2511.18525/) (2025)
- [CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation](https://mlsanity.com/paper/2602.20055/) (2026)
- [Seeing Where to Deploy: Metric RGB-Based Traversability Analysis for Aerial-to-Ground Hidden Space Inspection](https://mlsanity.com/paper/2603.14639/) (2026)
- [Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning](https://mlsanity.com/paper/2605.31481/) (2026)
- [VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models](https://mlsanity.com/paper/2606.13460/) (2026)
- [Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.21387/) (2026)
- [Unsupervised Memory-Enhanced Video Transformers: Obstacle Detection for Autonomous Agricultural Rover](https://mlsanity.com/paper/2606.26151/) (2026)
- [GAFT: Geo-Anchored Fine-Tuning for Hazard Identification from Rare Failures](https://mlsanity.com/paper/2608.30858/) (2026)
- [Which Terrain Is Better? Preference Learning with VLM Prototypes for Off-Road Traversability Ranking](https://mlsanity.com/paper/2609.23673/) (2026)
- [FLINT: Fast Lightweight Inference for Traversability](https://mlsanity.com/paper/2609.26857/) (2026)

## Abstract (arXiv)

Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-dimensional features from pre-trained self-supervised models, which implicitly encode semantic information that massively simplifies the learning task. Further, the development of an online scheme for supervision generator enables concurrent training and inference of the learned model in the wild. We demonstrate our approach through diverse real-world deployments in forests, parks, and grasslands. Our system is able to bootstrap the traversable terrain segmentation in less than 5 min of in-field training time, enabling the robot to navigate in complex, previously unseen outdoor terrains. Code: https://bit.ly/498b0CV - Project page:https://bit.ly/3M6nMHH