FocusNav: Spatial Selective Attention with Waypoint Guidance for Humanoid Local Navigation

TL;DR

FocusNav introduces a novel spatial attention framework with waypoint guidance and stability-aware gating, significantly improving humanoid robot navigation success in complex, dynamic environments by balancing long-range goals and immediate safety.

Contribution

The paper presents FocusNav, a new attention-based navigation method with waypoint-guided cross-attention and stability-aware gating, enhancing robustness and success rates in humanoid navigation tasks.

Findings

Improves navigation success rates in complex environments

Outperforms baseline methods in collision avoidance

Enhances motion stability during navigation

Abstract

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.