IntentReact: Guiding Reactive Object-Centric Navigation via Topological Intent

TL;DR

IntentReact is a navigation framework that combines global topological guidance with reactive control, enabling robots to navigate more effectively by reorienting based on intent signals derived from semantic and topological understanding.

Contribution

The paper introduces a novel intent-conditioned navigation approach that bridges global topological planning with local reactive control using a low-dimensional directional signal.

Findings

Improved navigation success over prior methods

Enhanced reorientation capabilities in complex environments

Robustness to partial observability and misleading local observations

Abstract

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.