FocusNav: Spatial Selective Attention with Waypoint Guidance for Humanoid Local Navigation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Collision AvoidanceSimilar Papers 제목 키워드 기반
MMP-A*: Multimodal Perception Enhanced Incremental Heuristic Search on Path Planning
Autonomous path planning requires a synergy between global reasoning and geometric precision, especially in complex or cluttered environments. While classical A* is valued for its optimality, it incurs prohibitive comput…
Computational EfficiencyNEAT: Neural Attention Fields for End-to-End Autonomous Driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel representation that enables such rea…
Autonomous DrivingCARLA longest6Imitation LearningNovel View Synthesis3D HAMSTER: Bridging Planning and Control in Hierarchical Vision Language Action Models through 3D Trajectory Guidance
Hierarchical Vision-Language-Action (VLA) models decouple high-level planning from low-level control to improve generalization in robot manipulation. Recent work in this paradigm uses 2D end-effector trajectories predict…
Trajectory PredictionRobot ManipulationPoint CloudsSmartWay: Enhanced Waypoint Prediction and Backtracking for Zero-Shot Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) in continuous environments requires agents to interpret natural language instructions while navigating unconstrained 3D spaces. Existing VLN-CE frameworks rely on a two-stage approach…
Language ModelingLanguage ModellingLarge Language ModelVision and Language NavigationReal-time Traffic Simulation and Management for Large-scale Urban Air Mobility: Integrating Route Guidance and Collision Avoidance
Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM will likely be deployed in specific areas, e.g., inter-transfer traffic between suburbs and city centers. However, large-scale UAM oper…
Collision AvoidanceComputational EfficiencyManagement