WROOM: An Autonomous Driving Approach for Off-Road Navigation
On the map: End-to-end autonomous driving → Reinforcement learning for driving
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
Key facts. Cited by 2 papers in the map (data through 7 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.
Builds on
16 of 29 references.
References · 16
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2016Generative Adversarial Imitation Learningcited by 1,576
- 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 494
- 2015Policy Distillationcited by 414
- 2019Control Barrier Functions: Theory and Applicationscited by 332
- 2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 131
- 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
- 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 44
- 2022VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamicscited by 19
- 2023Toward Wheeled Mobility on Vertically Challenging Terrain: Platforms, Datasets, and Algorithmscited by 16
- 2017Agile Autonomous Driving using End-to-End Deep Imitation Learningcited by 9
4 more
- 2023Safe Deep Policy Adaptationcited by 9
- 2023Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racingcited by 4
- 2023Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machinecited by 4
- 2020UAV Target Tracking in Urban Environments Using Deep Reinforcement Learningcited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2022Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
- 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
- 2024CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
- 2024From Imitation to Exploration: End-to-end Autonomous Driving based on World Model
- 2026PlanRL: A Trajectory Planning Architecture for Reinforcement Learning-based Driving Experts
- 2019Driving in Dense Traffic with Model-Free Reinforcement Learning
- 2019Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning
- 2025AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models