Achieving robust humanoid hiking in complex, unstructured environments requires transitioning from reactive proprioception to proactive perception. However, integrating exteroception remains a significant challenge: mapping-based methods suffer from state estimation drift; for instance, LiDAR-based methods do not handle torso jitter well. Existing end-to-end approaches often struggle with scalability and training complexity; specifically, some previous works using virtual obstacles are implemented case-by-case. In this work, we present \textit{Hiking in the Wild}, a scalable, end-to-end parkour perceptive framework designed for robust humanoid hiking. To ensure safety and training stability, we introduce two key mechanisms: a foothold safety mechanism combining scalable \textit{Terrain Edge Detection} with \textit{Foot Volume Points} to prevent catastrophic slippage on edges, and a \textit{Flat Patch Sampling} strategy that mitigates reward hacking by generating feasible navigation targets. Our approach utilizes a single-stage reinforcement learning scheme, mapping raw depth inputs and proprioception directly to joint actions, without relying on external state estimation. Extensive field experiments on a full-size humanoid demonstrate that our policy enables robust traversal of complex terrains at speeds up to 2.5 m/s. The training and deployment code is open-sourced to facilitate reproducible research and deployment on real robots with minimal hardware modifications.

翻译：在复杂非结构化环境中实现稳健的人形机器人徒步运动，需要从反应性本体感知转向主动性环境感知。然而，融合外部感知仍面临重大挑战：基于地图构建的方法存在状态估计漂移问题，例如基于激光雷达的方法难以有效处理躯干抖动。现有端到端方法常受限于可扩展性和训练复杂性；特别是先前采用虚拟障碍物的研究多为个案实现。本研究提出《野外徒步》，一种专为稳健人形机器人徒步设计的可扩展端到端感知跑酷框架。为确保安全性与训练稳定性，我们引入两个关键机制：结合可扩展《地形边缘检测》与《足部体积点》的立足点安全机制以防止边缘灾难性滑移，以及通过生成可行导航目标来缓解奖励破解的《平坦区域采样》策略。该方法采用单阶段强化学习方案，直接将原始深度输入与本体感知映射至关节动作，无需依赖外部状态估计。在全尺寸人形机器人上的大量实地实验表明，我们的策略能以最高2.5米/秒的速度稳健穿越复杂地形。训练与部署代码已开源，以促进可复现研究，并支持通过最小硬件改造在真实机器人上部署。