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.

翻译：腿式机器人必须在多样化的非结构化地形上展现出稳健且灵活的运动能力，这一挑战在盲运动设置下尤为突出，因为地形信息不可用。本研究提出了一种分层强化学习框架，该框架利用地形专用策略和课程学习来增强复杂环境中的敏捷性和跟踪性能。我们在仿真环境中验证了该方法，结果显示，相较于通用策略，我们的方法在成功率上提升了高达16%，并在速度目标增加时实现了更低的跟踪误差，特别是在低摩擦和不连续地形上，证明了其在混合地形场景中具有卓越的适应性和鲁棒性。