Robust local navigation in unstructured and dynamic environments remains a significant challenge for humanoid robots, requiring a delicate balance between long-range navigation targets and immediate motion stability. In this paper, we propose FocusNav, a spatial selective attention framework that adaptively modulates the robot's perceptual field based on navigational intent and real-time stability. FocusNav features a Waypoint-Guided Spatial Cross-Attention (WGSCA) mechanism that anchors environmental feature aggregation to a sequence of predicted collision-free waypoints, ensuring task-relevant perception along the planned trajectory. To enhance robustness in complex terrains, the Stability-Aware Selective Gating (SASG) module autonomously truncates distal information when detecting instability, compelling the policy to prioritize immediate foothold safety. Extensive experiments on the Unitree G1 humanoid robot demonstrate that FocusNav significantly improves navigation success rates in challenging scenarios, outperforming baselines in both collision avoidance and motion stability, achieving robust navigation in dynamic and complex environments.

翻译：在非结构化和动态环境中实现鲁棒的局部导航对于仿人机器人仍是一项重大挑战，这需要在长程导航目标与即时运动稳定性之间取得精细平衡。本文提出FocusNav，一种空间选择性注意力框架，能够根据导航意图和实时稳定性自适应地调节机器人的感知域。FocusNav的核心是路径点引导空间交叉注意力（WGSCA）机制，该机制将环境特征聚合锚定到一系列预测的无碰撞路径点上，确保沿规划轨迹进行任务相关的感知。为增强复杂地形下的鲁棒性，稳定性感知选择性门控（SASG）模块在检测到不稳定状态时自动截断远端信息，迫使策略优先考虑即时立足点安全。在宇树G1仿人机器人上进行的大量实验表明，FocusNav在挑战性场景中显著提升了导航成功率，在避障和运动稳定性方面均优于基线方法，实现了动态复杂环境下的鲁棒导航。