用强化学习实现全尺寸越野车高速自主驾驶，支持零样本仿真到现实迁移。
TADPO: Reinforcement Learning Goes Off-road
- 提出TADPO算法，结合离线与在线轨迹指导策略学习
- 在模拟和真实车辆上实现高速越障与陡坡行驶
- 首个在全尺寸越野车上部署的强化学习自动驾驶系统
越野自主驾驶面临未测绘、动态多变地形的挑战，需具备长程规划与自适应控制能力。强化学习可通过交互直接学习控制策略，但因任务周期长、奖励信号弱，传统方法难以应用。本文提出TADPO，一种扩展PPO的策略梯度方法，利用离线轨迹提供教师引导，结合在线轨迹进行学生探索。基于此，构建了视觉驱动的端到端强化学习系统，可实现高速越野行驶，应对极端坡度与障碍密集环境。在仿真中验证性能，并成功实现零样本仿真到现实的迁移，在全尺寸越野车上完成部署。据我们所知，这是首个在全尺寸越野平台上运行强化学习策略的工作。
原文摘要 · Abstract (English)
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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