Object-goal visual navigation requires robots to reason over semantic structure and act effectively under partial observability. Recent approaches based on object-level topological maps enable long-horizon navigation without dense geometric reconstruction, but their execution remains limited by the gap between global topological guidance and local perception-driven control. In particular, local decisions are made solely from the current egocentric observation, without access to information beyond the robot's field of view. As a result, the robot may persist along its current heading even when initially oriented away from the goal, moving toward directions that do not decrease the global topological distance. In this work, we propose IntentReact, an intent-conditioned object-centric navigation framework that introduces a compact interface between global topological planning and reactive object-centric control. Our approach encodes global topological guidance as a low-dimensional directional signal, termed intent, which conditions a learned waypoint prediction policy to bias navigation toward topologically consistent progression. This design enables the robot to promptly reorient when local observations are misleading, guiding motion toward directions that decrease global topological distance while preserving the reactivity and robustness of object-centric control. We evaluate the proposed framework through extensive experiments, demonstrating improved navigation success and execution quality compared to prior object-centric navigation methods.

翻译：目标物体视觉导航要求机器人在部分可观测条件下推理语义结构并有效行动。现有基于物体级拓扑地图的方法无需密集几何重建即可实现长时域导航，但其执行仍受限于全局拓扑引导与局部感知驱动控制之间的鸿沟。具体而言，局部决策完全依赖当前自我中心观测，无法获取机器人视野之外的信息。这导致即使初始朝向偏离目标方向，机器人仍可能沿当前朝向持续前进，移向无法缩短全局拓扑距离的方向。本文提出意图反应，一种意图条件化的物体中心导航框架，在全局拓扑规划与反应式物体中心控制之间引入紧凑接口。该方法将全局拓扑引导编码为低维方向信号（称为意图），用于调节学习到的路径点预测策略，使其偏向于符合拓扑一致性的导航方向。这种设计使得机器人能在局部观测产生误导时迅速调整朝向，引导运动朝向缩短全局拓扑距离的方向，同时保持物体中心控制的反应性与鲁棒性。通过广泛实验评估，我们证明了所提框架相较于先前的物体中心导航方法在导航成功率和执行质量上的提升。