ProFocus: Proactive Perception and Focused Reasoning in Vision-and-Language Navigation

TL;DR

ProFocus introduces a proactive perception and focused reasoning framework for vision-and-language navigation, leveraging structured semantic maps and selective historical reasoning to improve efficiency and accuracy in complex environments.

Contribution

It presents a training-free, collaborative approach combining LLMs and VLMs for structured perception and targeted reasoning, advancing zero-shot VLN performance.

Findings

Achieves state-of-the-art zero-shot results on R2R and REVERIE benchmarks.

Effectively transforms panoramic views into semantic maps for better perception.

Focuses reasoning on high-value waypoints, reducing computational complexity.

Abstract

Vision-and-Language Navigation (VLN) requires agents to accurately perceive complex visual environments and reason over navigation instructions and histories. However, existing methods passively process redundant visual inputs and treat all historical contexts indiscriminately, resulting in inefficient perception and unfocused reasoning. To address these challenges, we propose \textbf{ProFocus}, a training-free progressive framework that unifies \underline{Pro}active Perception and \underline{Focus}ed Reasoning through collaboration between large language models (LLMs) and vision-language models (VLMs). For proactive perception, ProFocus transforms panoramic observations into structured ego-centric semantic maps, enabling the orchestration agent to identify missing visual information needed for reliable decision-making, and to generate targeted visual queries with corresponding focus regions that guide the perception agent to acquire the required observations. For focused reasoning, we propose Branch-Diverse Monte Carlo Tree Search (BD-MCTS) to identify top-$k$ high-value waypoints from extensive historical candidates. The decision agent focuses reasoning on the historical contexts associated with these waypoints, rather than considering all historical waypoints equally. Extensive experiments validate the effectiveness of ProFocus, achieving state-of-the-art performance among zero-shot methods on R2R and REVERIE benchmarks.