# WROOM: An Autonomous Driving Approach for Off-Road Navigation

arXiv: https://arxiv.org/abs/2404.08855 · HTML: https://mlsanity.com/paper/2404.08855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Legged Locomotion in Challenging Terrains using Egocentric Vision](https://mlsanity.com/paper/2211.07638/) (2022)
- [Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios](https://mlsanity.com/paper/2212.11419/) (2022)
- [Safe Reinforcement Learning Using Robust Control Barrier Functions](https://mlsanity.com/paper/2110.05415/) (2021)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithms](https://mlsanity.com/paper/2303.00998/) (2023)
- [Agile Autonomous Driving using End-to-End Deep Imitation Learning](https://mlsanity.com/paper/1709.07174/) (2017)
- [Safe Deep Policy Adaptation](https://mlsanity.com/paper/2310.08602/) (2023)
- [Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racing](https://mlsanity.com/paper/2303.02267/) (2023)
- [Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine](https://mlsanity.com/paper/2303.08235/) (2023)
- [UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.10934/) (2020)

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

- [TADPO: Reinforcement Learning Goes Off-road](https://mlsanity.com/paper/2603.05995/) (2026)
- [MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving](https://mlsanity.com/paper/2609.20747/) (2026)

## Abstract (arXiv)

Off-road navigation is a challenging problem both at the planning level to get a smooth trajectory and at the control level to avoid flipping over, hitting obstacles, or getting stuck at a rough patch. There have been several recent works using classical approaches involving depth map prediction followed by smooth trajectory planning and using a controller to track it. We design an end-to-end reinforcement learning (RL) system for an autonomous vehicle in off-road environments using a custom-designed simulator in the Unity game engine. We warm-start the agent by imitating a rule-based controller and utilize Proximal Policy Optimization (PPO) to improve the policy based on a reward that incorporates Control Barrier Functions (CBF), facilitating the agent's ability to generalize effectively to real-world scenarios. The training involves agents concurrently undergoing domain-randomized trials in various environments. We also propose a novel simulation environment to replicate off-road driving scenarios and deploy our proposed approach on a real buggy RC car. Videos and additional results: https://sites.google.com/view/wroom-utd/home