CLASH: Collaborative Large-Small Hierarchical Framework for Continuous Vision-and-Language Navigation

TL;DR

CLASH introduces a hierarchical framework combining large and small models for improved continuous vision-and-language navigation, achieving state-of-the-art results in simulation and real-world environments.

Contribution

The paper proposes a novel collaborative hierarchical framework integrating large and small models with adaptive decision fusion for VLN tasks.

Findings

Achieves state-of-the-art performance on VLN-CE leaderboard

Demonstrates robustness in real-world navigation scenarios

Improves success rate and path efficiency over previous methods

Abstract

Vision-and-Language Navigation (VLN) requires robots to follow natural language instructions and navigate complex environments without prior maps. While recent vision-language large models demonstrate strong reasoning abilities, they often underperform task-specific panoramic small models in VLN tasks. To address this, we propose CLASH (Collaborative Large-Small Hierarchy), a VLN-CE framework that integrates a reactive small-model planner (RSMP) with a reflective large-model reasoner (RLMR). RSMP adopts a causal-learning-based dual-branch architecture to enhance generalization, while RLMR leverages panoramic visual prompting with chain-of-thought reasoning to support interpretable spatial understanding and navigation. We further introduce an uncertainty-aware collaboration mechanism (UCM) that adaptively fuses decisions from both models. For obstacle avoidance, in simulation, we replace the rule-based controller with a fully learnable point-goal policy, and in real-world deployment, we design a LiDAR-based clustering module for generating navigable waypoints and pair it with an online SLAM-based local controller. CLASH achieves state-of-the-art (SoTA) results (ranking 1-st) on the VLN-CE leaderboard, significantly improving SR and SPL on the test-unseen set over the previous SoTA methods. Real-world experiments demonstrate CLASH's strong robustness, validating its effectiveness in both simulation and deployment scenarios.