TADPO: Reinforcement Learning Goes Off-road
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908870066044928 |
|---|---|
| author | Wu, Zhouchonghao Song, Raymond Mundheda, Vedant Navarro-Serment, Luis E. Schoenborn, Christof Schneider, Jeff |
| author_facet | Wu, Zhouchonghao Song, Raymond Mundheda, Vedant Navarro-Serment, Luis E. Schoenborn, Christof Schneider, Jeff |
| contents | Off-road autonomous driving poses significant challenges such as navigating unmapped, variable terrain with uncertain and diverse dynamics. Addressing these challenges requires effective long-horizon planning and adaptable control. Reinforcement Learning (RL) offers a promising solution by learning control policies directly from interaction. However, because off-road driving is a long-horizon task with low-signal rewards, standard RL methods are challenging to apply in this setting. We introduce TADPO, a novel policy gradient formulation that extends Proximal Policy Optimization (PPO), leveraging off-policy trajectories for teacher guidance and on-policy trajectories for student exploration. Building on this, we develop a vision-based, end-to-end RL system for high-speed off-road driving, capable of navigating extreme slopes and obstacle-rich terrain. We demonstrate our performance in simulation and, importantly, zero-shot sim-to-real transfer on a full-scale off-road vehicle. To our knowledge, this work represents the first deployment of RL-based policies on a full-scale off-road platform. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05995 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TADPO: Reinforcement Learning Goes Off-road Wu, Zhouchonghao Song, Raymond Mundheda, Vedant Navarro-Serment, Luis E. Schoenborn, Christof Schneider, Jeff Robotics Artificial Intelligence Machine Learning Off-road autonomous driving poses significant challenges such as navigating unmapped, variable terrain with uncertain and diverse dynamics. Addressing these challenges requires effective long-horizon planning and adaptable control. Reinforcement Learning (RL) offers a promising solution by learning control policies directly from interaction. However, because off-road driving is a long-horizon task with low-signal rewards, standard RL methods are challenging to apply in this setting. We introduce TADPO, a novel policy gradient formulation that extends Proximal Policy Optimization (PPO), leveraging off-policy trajectories for teacher guidance and on-policy trajectories for student exploration. Building on this, we develop a vision-based, end-to-end RL system for high-speed off-road driving, capable of navigating extreme slopes and obstacle-rich terrain. We demonstrate our performance in simulation and, importantly, zero-shot sim-to-real transfer on a full-scale off-road vehicle. To our knowledge, this work represents the first deployment of RL-based policies on a full-scale off-road platform. |
| title | TADPO: Reinforcement Learning Goes Off-road |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.05995 |