用强化学习让小车在极限状态下漂移，真实场景表现稳定。
Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits
- GPU加速并行仿真+系统化领域随机化提升泛化能力
- 实现在模拟与真实小车上精准跟踪复杂轨迹，侧滑角可控
- 适合自动驾驶极限操控、智能车辆控制研究者
漂移是指在高侧滑角下进行可控的车辆运动，对在摩擦极限下安全应对紧急情况至关重要。尽管近期强化学习方法在漂移控制方面展现出潜力，但其在仿真到现实之间的迁移性能仍面临显著挑战，许多在仿真中表现良好的策略在物理系统上无法有效运行。本文提出一种基于GPU加速并行仿真和系统性领域随机化的强化学习框架，有效弥合了这一差距。该方法在模拟环境及自研开源的1/10比例独立轮驱动（IWD）遥控车平台上进行了验证，该平台具备独立轮速控制能力。实验涵盖从稳态圆周漂移到方向切换、变曲率路径跟踪等多种场景，结果表明，该方法在仿真与真实环境中均能实现精确轨迹跟踪，并全程保持可控的侧滑角。
原文摘要 · Abstract (English)
Drifting, characterized by controlled vehicle motion at high sideslip angles, is crucial for safely handling emergency scenarios at the friction limits. While recent reinforcement learning approaches show promise for drifting control, they struggle with the significant simulation-to-reality gap, as policies that perform well in simulation often fail when transferred to physical systems. In this paper, we present a reinforcement learning framework with GPU-accelerated parallel simulation and systematic domain randomization that effectively bridges the gap. The proposed approach is validated on both simulation and a custom-designed and open-sourced 1/10 scale Individual Wheel Drive (IWD) RC car platform featuring independent wheel speed control. Experiments across various scenarios from steady-state circular drifting to direction transitions and variable-curvature path following demonstrate that our approach achieves precise trajectory tracking while maintaining controlled sideslip angles throughout complex maneuvers in both simulated and real-world environments.
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