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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.08855 |
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| _version_ | 1866929313232715776 |
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| author | Kalaria, Dvij Sharma, Shreya Bhagat, Sarthak Xue, Haoru Dolan, John M. |
| author_facet | Kalaria, Dvij Sharma, Shreya Bhagat, Sarthak Xue, Haoru Dolan, John M. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | WROOM: An Autonomous Driving Approach for Off-Road Navigation Kalaria, Dvij Sharma, Shreya Bhagat, Sarthak Xue, Haoru Dolan, John M. Robotics Machine Learning 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 |
| title | WROOM: An Autonomous Driving Approach for Off-Road Navigation |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2404.08855 |