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：具身双足机器人已具备较强的运动能力，但在复杂地形中实现可靠导航仍具挑战性——障碍规避需与动力学可行的运动协调。本文提出GuideWalk框架，一种统一的端到端方案，通过融合可穿越性感知导航引导与地形自适应运动教师模型，实现双足机器人导航。具体而言，我们设计了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，从而支持多样化环境下的鲁棒规划。我们提出复合式教师蒸馏方案，将目标导向指令与动力学一致的动作聚合蒸馏至单一策略。为增强鲁棒性，蒸馏策略通过强化学习与辅助行为克隆目标进行精炼，该目标在保留教师策略优势行为的同时促进探索。实验表明，GuideWalk在保持稳定双足运动的同时，实现了稳健有效的导航。