Humanoid robots have achieved strong locomotion capabilities, but reliable navigation on versatile terrains remains challenging because obstacle avoidance must be coordinated with dynamically feasible motion. In this work, we present GuideWalk, a unified end-to-end framework that integrates traversability-aware navigation guidance with terrain-adaptive locomotion teacher for humanoid navigation. Specifically, we introduce a navigation module that provides explicit velocity guidance, decoupling obstacle avoidance from terrain conditions to enable robust planning across diverse environments. We propose a composite teacher distillation scheme, where goal-directed commands and dynamically consistent actions are aggregated and distilled into a single policy. To further improve robustness, the distilled policy is refined with reinforcement learning and an auxiliary behavior cloning objective, which promotes exploration while preserving desirable teacher behaviors. Experiments demonstrate that GuideWalk achieves stable and effective navigation while maintaining stable humanoid locomotion.

翻译：人形机器人已具备强大的运动能力，但在多样化地形上实现可靠导航仍具挑战性，因为障碍规避必须与动态可行的运动协调。本文提出GuideWalk——一个统一的端到端框架，将可通行性感知的导航引导与地形自适应运动教师相结合，用于人形机器人导航。具体而言，我们引入一个提供显式速度引导的导航模块，将障碍规避与地形条件解耦，从而在多样环境中实现鲁棒规划。我们提出一种复合式教师蒸馏方案，将目标导向指令与动态一致动作进行聚合，并蒸馏为单一策略。为进一步提升鲁棒性，通过强化学习与辅助行为克隆目标对蒸馏策略进行优化，该策略在保留教师理想行为的同时促进探索。实验表明，GuideWalk在保持人形机器人稳定运动的同时，实现了稳定有效的导航。