IntentReact: Guiding Reactive Object-Centric Navigation via Topological Intent

2026-03-26

AI Summary

This work addresses the disconnect between global topological planning and local reactive control in object-goal visual navigation by proposing an intention-guided, reactive object-centric navigation framework. The method introduces a compact “intention” interface that encodes object-based topological maps into low-dimensional directional signals, effectively bridging the gap between high-level planning and low-level reactive control. Integrated with a learned waypoint prediction strategy, this approach enables topology-consistent guidance for local navigation. Experimental results demonstrate that the proposed framework significantly improves both navigation success rates and execution efficiency, outperforming existing object-centric navigation methods.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Localization, Mapping, and Navigation
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• object-goal navigation
• topological planning
• reactive control
• partial observability
• egocentric observation

Innovation

• topological intent
• object-centric navigation
• reactive control
• waypoint prediction
• visual navigation