TADPO: Reinforcement Learning Goes Off-road

• URL: http://arxiv.org/abs/2603.05995v1
• Date: Fri, 06 Mar 2026 07:55:01 GMT
• Title: TADPO: Reinforcement Learning Goes Off-road
• Abstract summary: 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.<n>To our knowledge, this work represents the first deployment of RL-based policies on a full-scale off-road platform.
• Score: 3.6550297724888328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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