让人形机器人在复杂环境中更稳地导航，靠的是注意力聚焦和路径引导。
FocusNav: Spatial Selective Attention with Waypoint Guidance for Humanoid Local Navigation
- 通过路径点引导注意力，只关注重要区域
- 检测到不稳时自动忽略远处信息，确保脚下安全
- 适合需要稳定行走的人形机器人应用
在非结构化且动态的环境中实现稳健的局部导航仍是人形机器人的重大挑战，需在远距离目标与即时运动稳定性之间取得平衡。本文提出FocusNav，一种空间选择性注意力框架，可基于导航意图和实时稳定性自适应调节机器人的感知范围。该框架采用路径点引导的空间交叉注意力（WGSCA）机制，将环境特征聚合锚定于一系列预测的无碰撞路径点，确保沿规划轨迹的任务相关感知。为增强复杂地形下的鲁棒性，稳定性感知选择门控（SASG）模块在检测到不稳定时自动截断远距离信息，迫使策略优先考虑即时落脚安全。在Unitree G1人形机器人上的大量实验表明，FocusNav显著提升了复杂场景下的导航成功率，优于基线方法，在避障和运动稳定性方面均表现更优，实现了动态复杂环境中的鲁棒导航。
原文摘要 · Abstract (English)
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.
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