# Learning Ground Traversability from Simulations

arXiv: https://arxiv.org/abs/1709.05368 · HTML: https://mlsanity.com/paper/1709.05368/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2017.

## Built on by (most cited first, 20 of 20)

- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [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)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [Learning Visual Locomotion with Cross-Modal Supervision](https://mlsanity.com/paper/2211.03785/) (2022)
- [Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision](https://mlsanity.com/paper/2404.07110/) (2024)
- [SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience](https://mlsanity.com/paper/2409.13678/) (2024)
- [Neural Scene Representation for Locomotion on Structured Terrain](https://mlsanity.com/paper/2206.08077/) (2022)
- [RCA: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning](https://mlsanity.com/paper/2207.14460/) (2022)
- [Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control](https://mlsanity.com/paper/2305.00676/) (2023)
- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)
- [Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments](https://mlsanity.com/paper/2105.10937/) (2021)
- [URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments](https://mlsanity.com/paper/2309.08814/) (2023)
- [Learning multiobjective rough terrain traversability](https://mlsanity.com/paper/2203.16354/) (2022)
- [Walking = Traversable? : Traversability Prediction via Multiple Human Object Tracking under Occlusion](https://mlsanity.com/paper/2310.00242/) (2023)
- [FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud](https://mlsanity.com/paper/2406.16564/) (2024)
- [Wild-Drive: Off-Road Scene Captioning and Path Planning via Robust Multi-modal Routing and Efficient Large Language Model](https://mlsanity.com/paper/2603.00694/) (2026)
- [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)

## Abstract (arXiv)

Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.