# Complex Terrain Navigation via Model Error Prediction

arXiv: https://arxiv.org/abs/2111.09768 · HTML: https://mlsanity.com/paper/2111.09768/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [Deep Dynamics Models for Learning Dexterous Manipulation](https://mlsanity.com/paper/1909.11652/) (2019)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Combining Optimal Control and Learning for Visual Navigation in Novel Environments](https://mlsanity.com/paper/1903.02531/) (2019)
- [High-Speed Robot Navigation using Predicted Occupancy Maps](https://mlsanity.com/paper/2012.12142/) (2020)

## Built on by (most cited first, 2 of 2)

- [CoNVOI: Context-aware Navigation using Vision Language Models in Outdoor and Indoor Environments](https://mlsanity.com/paper/2403.15637/) (2024)
- [Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation](https://mlsanity.com/paper/2404.17820/) (2024)

## Abstract (arXiv)

Robot navigation traditionally relies on building an explicit map that is used to plan collision-free trajectories to a desired target. In deformable, complex terrain, using geometric-based approaches can fail to find a path due to mischaracterizing deformable objects as rigid and impassable. Instead, we learn to predict an estimate of traversability of terrain regions and to prefer regions that are easier to navigate (e.g., short grass over small shrubs). Rather than predicting collisions, we instead regress on realized error compared to a canonical dynamics model. We train with an on-policy approach, resulting in successful navigation policies using as little as 50 minutes of training data split across simulation and real world. Our learning-based navigation system is a sample efficient short-term planner that we demonstrate on a Clearpath Husky navigating through a variety of terrain including grassland and forest