DecoVLN: Decoupling Observation, Reasoning, and Correction for Vision-and-Language Navigation

TL;DR

DecoVLN introduces a novel framework for vision-and-language navigation that enhances long-term memory construction and error correction, leading to more robust and accurate navigation in complex environments.

Contribution

The paper proposes a new framework that decouples observation, reasoning, and correction, with adaptive memory refinement and state-action correction strategies for improved VLN performance.

Findings

Effective long-term memory optimization improves navigation accuracy.

State-action correction reduces compounding errors.

Real-world deployment demonstrates practical robustness.

Abstract

Vision-and-Language Navigation (VLN) requires agents to follow long-horizon instructions and navigate complex 3D environments. However, existing approaches face two major challenges: constructing an effective long-term memory bank and overcoming the compounding errors problem. To address these issues, we propose DecoVLN, an effective framework designed for robust streaming perception and closed-loop control in long-horizon navigation. First, we formulate long-term memory construction as an optimization problem and introduce adaptive refinement mechanism that selects frames from a historical candidate pool by iteratively optimizing a unified scoring function. This function jointly balances three key criteria: semantic relevance to the instruction, visual diversity from the selected memory, and temporal coverage of the historical trajectory. Second, to alleviate compounding errors, we introduce a state-action pair-level corrective finetuning strategy. By leveraging geodesic distance between states to precisely quantify deviation from the expert trajectory, the agent collects high-quality state-action pairs in the trusted region while filtering out the polluted data with low relevance. This improves both the efficiency and stability of error correction. Extensive experiments demonstrate the effectiveness of DecoVLN, and we have deployed it in real-world environments.