全局-局部注意力分解用于人形感知运动中的地形编码
Global-Local Attention Decomposition for Terrain Encoding in Humanoid Perceptive Locomotion
AI总结：
提出全局-局部注意力分解（GLAD）方法，通过粗到细编码器分离全局地形感知和局部立足点选择，实现人形机器人在稀疏立足点和受限环境中的鲁棒运动。
摘要：
尽管强化学习显著推进了人形运动，感知策略在稀疏立足点地形和受限环境中仍然存在困难。在这些场景中成功需要广泛的地形感知和精确的立足点选择，而传统编码器常常纠缠这两种感知角色。为了解决这一挑战，我们提出了用于人形运动地形编码的全局-局部注意力分解（GLAD）。通过基于机器人中心高程图的粗到细编码器实现，GLAD明确分离了这些目标：全局注意力分支利用注意力池化总结周围地形上下文，而状态条件局部注意力分支稀疏化并编码精确的立足点相关几何。这种显式注意力分解防止了细粒度空间线索的稀释，同时减少了训练开销。实验表明，GLAD能够在具有挑战性的间隙、踏脚石和楼梯上实现可靠运动。此外，学习到的策略表现出涌现的地形响应行为，在简单速度指令下自主跟随狭窄路径并避开障碍物，无需显式导航规划器。在搭载机载LiDAR的Unitree G1人形机器人上的实际部署中，所提方法在多种稀疏立足点和障碍物丰富领域实现了鲁棒的零样本仿真到现实迁移。
英文摘要：
Although reinforcement learning has significantly advanced humanoid locomotion, perceptive policies still struggle on sparse-foothold terrain and constrained environments. Success in these scenarios requires both broad terrain awareness and precise foothold selection, two perceptual roles that conventional encoders often entangle. To address this challenge, we propose Global-Local Attention Decomposition (GLAD) for terrain encoding in humanoid locomotion. Realized by a coarse-to-fine encoder over a robot-centric elevation map, GLAD explicitly separates these objectives: a global attention branch uses attention pooling to summarize the surrounding terrain context, while a local attention branch sparsifies the local features by terrain saliency and applies state-conditioned attention to encode precise foothold-relevant geometry. This explicit attention decomposition prevents the dilution of fine-grained spatial cues while reducing training overhead. Experiments demonstrate that GLAD enables reliable locomotion over challenging gaps, stepping stones, and stairs. Furthermore, the learned policy exhibits emergent terrain-responsive behaviors, autonomously following narrow paths and avoiding obstacles under forward-velocity commands alone, without explicit navigation planners. In real-world deployment on a Unitree G1 humanoid robot using onboard LiDAR, the proposed method achieves robust zero-shot sim-to-real transfer across diverse sparse-foothold and obstacle-rich domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
