让机器人在复杂地形中自适应行走，提升越障能力。
Learning Terrain-Specialized Policies for Adaptive Locomotion in Challenging Environments
- 分层强化学习+课程学习，针对不同地形训练专用策略
- 速度越高越明显，成功率提升16%，低摩擦地形表现更优
- 适合需要灵活应对多变地形的机器人应用
腿式机器人需在多样且非结构化的地形中表现出鲁棒和敏捷的运动能力，这一挑战在无法获取地形信息的盲走设置下尤为严峻。本文提出一种分层强化学习框架，利用地形专用策略与课程学习，提升复杂环境中的敏捷性与轨迹跟踪性能。我们在仿真环境中验证了该方法，结果表明，相比通用策略，本方法在成功率上最高提升16%，且随着速度目标提高，跟踪误差更低，尤其在低摩擦和不连续地形上表现更优，证明了其在混合地形场景下的优越适应性与鲁棒性。
原文摘要 · Abstract (English)
Legged robots must exhibit robust and agile locomotion across diverse, unstructured terrains, a challenge exacerbated under blind locomotion settings where terrain information is unavailable. This work introduces a hierarchical reinforcement learning framework that leverages terrain-specialized policies and curriculum learning to enhance agility and tracking performance in complex environments. We validated our method on simulation, where our approach outperforms a generalist policy by up to 16% in success rate and achieves lower tracking errors as the velocity target increases, particularly on low-friction and discontinuous terrains, demonstrating superior adaptability and robustness across mixed-terrain scenarios.
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