TADPO: Reinforcement Learning Goes Off-road

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.