Agile humanoid locomotion in complex 3D en- vironments requires balancing perceptual fidelity with com- putational efficiency, yet existing methods typically rely on rigid sensing configurations. We propose ADAPT (Adaptive dual-projection architecture for perceptive traversal), which represents the environment using a horizontal elevation map for terrain geometry and a vertical distance map for traversable- space constraints. ADAPT further treats its spatial sensing range as a learnable action, enabling the policy to expand its perceptual horizon during fast motion and contract it in cluttered scenes for finer local resolution. Compared with voxel-based baselines, ADAPT drastically reduces observation dimensionality and computational overhead while substantially accelerating training. Experimentally, it achieves successful zero-shot transfer to a Unitree G1 Humanoid and signifi- cantly outperforms fixed-range baselines, yielding highly robust traversal across diverse 3D environtmental challenges.

翻译：复杂三维环境中的敏捷人形机器人运动需要在感知保真度与计算效率之间取得平衡，然而现有方法通常依赖于固定的传感配置。我们提出了ADAPT（面向感知穿越的自适应双投影架构），该架构使用水平高程图表示地形几何，并使用垂直距离图表示可穿越空间约束。ADAPT进一步将其空间感知范围视为可学习的动作，使策略能够在快速运动时扩展其感知范围，并在杂乱场景中收缩感知范围以获得更精细的局部分辨率。与基于体素的基线方法相比，ADAPT大幅降低了观测维度和计算开销，同时显著加快了训练速度。实验表明，该方法成功实现了对Unitree G1人形机器人的零样本迁移，并显著优于固定感知范围的基线方法，在多样化的三维环境挑战中实现了高度鲁棒的穿越。