# Learning Autonomy: Off-Road Navigation Enhanced by Human Input

arXiv: https://arxiv.org/abs/2502.18760 · HTML: https://mlsanity.com/paper/2502.18760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2209.05722/) (2022)
- [Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation](https://mlsanity.com/paper/2302.00134/) (2023)
- [Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments](https://mlsanity.com/paper/2210.00153/) (2022)
- [Calibration of Human Driving Behavior and Preference Using Naturalistic Traffic Data](https://mlsanity.com/paper/2105.01820/) (2021)

## Built on by (most cited first, 1 of 1)

- [Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers](https://mlsanity.com/paper/2509.05201/) (2025)

## Abstract (arXiv)

In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as bushes and puddles. In this work, we present a novel learning-based local planner that addresses these challenges by directly capturing human driving nuances from real-world demonstrations using only a monocular camera. The key features of our planner are its ability to navigate in challenging off-road environments with various terrain types and its fast learning capabilities. By utilizing minimal human demonstration data (5-10 mins), it quickly learns to navigate in a wide array of off-road conditions. The local planner significantly reduces the real world data required to learn human driving preferences. This allows the planner to apply learned behaviors to real-world scenarios without the need for manual fine-tuning, demonstrating quick adjustment and adaptability in off-road autonomous driving technology.