AHead-VLN: A Hierarchical Reasoning Framework Guided by Foresight for Human-Aware Navigation

Abstract

In human-aware vision-and-language navigation (HA-VLN), agents need to follow instructions while navigating safely among people in crowded environments. Existing methods often struggle to effectively transform forward-looking risk prediction into decision-making basis, resulting in intelligent agents being prone to collisions in crowded scenarios and taking overly cautious actions, thereby reducing navigation efficiency. Moreover, it is difficult for high-level decisions based on social norms to be stably implemented in dynamic crowd scenarios, which can easily lead to behavioral jitter and planning deviation. In this work, we propose the AHead-VLN, a navigation framework that incorporates forward-looking risk information in the reasoning process. Specifically, we designed the path risk association (PRA) module, which compiles future risks into structured risk events aligned with each candidate path, making risk information decision-ready for the large language model (LLM) reasoning. To balance executability and decision stability, we have further designed the hierarchical LLM planner (HLP) with two specialized components. The ``Brain'' LLM makes route choices based on instructions and risks, while the ``Cerebellum'' LLM prepares executable action scripts for candidate routes and retrieves the appropriate script for execution. Experiments on the HA-VLN benchmark show that AHead-VLN improves success while enhancing safety compared to strong baselines.