Recent advancements in legged robot perceptive locomotion have shown promising progress. However, terrain-aware humanoid locomotion remains largely constrained to two paradigms: depth image-based end-to-end learning and elevation map-based methods. The former suffers from limited training efficiency and a significant sim-to-real gap in depth perception, while the latter depends heavily on multiple vision sensors and localization systems, resulting in latency and reduced robustness. To overcome these challenges, we propose a novel framework that tightly integrates three key components: (1) Terrain-Aware Locomotion Policy with a Blind Backbone, which leverages pre-trained elevation map-based perception to guide reinforcement learning with minimal visual input; (2) Multi-Modality Cross-Attention Transformer, which reconstructs structured terrain representations from noisy depth images; (3) Realistic Depth Images Synthetic Method, which employs self-occlusion-aware ray casting and noise-aware modeling to synthesize realistic depth observations, achieving over 30\% reduction in terrain reconstruction error. This combination enables efficient policy training with limited data and hardware resources, while preserving critical terrain features essential for generalization. We validate our framework on a full-sized humanoid robot, demonstrating agile and adaptive locomotion across diverse and challenging terrains.

翻译：近年来，腿式机器人的感知运动研究取得了显著进展。然而，地形感知的人形机器人运动仍主要受限于两种范式：基于深度图像的端到端学习方法和基于高程地图的方法。前者存在训练效率有限及深度感知方面显著的仿真到现实差距问题，而后者高度依赖多个视觉传感器与定位系统，导致延迟增加且鲁棒性降低。为克服这些挑战，我们提出一种新颖框架，该框架紧密整合了三个关键组件：(1) 具有盲主干的地形感知运动策略，该策略利用预训练的基于高程地图的感知能力，以最少的视觉输入指导强化学习；(2) 多模态跨注意力Transformer，用于从含噪深度图像中重建结构化地形表示；(3) 真实深度图像合成方法，该方法采用自遮挡感知的光线投射与噪声感知建模来合成真实的深度观测，使地形重建误差降低超过30%。这种组合使得在有限数据和硬件资源下能够高效训练策略，同时保留对泛化至关重要的关键地形特征。我们在全尺寸人形机器人上验证了所提框架，展示了其在多样且具有挑战性的地形上实现敏捷、自适应运动的能力。