Open-Nav: Exploring Zero-Shot Vision-and-Language Navigation in Continuous Environment with Open-Source LLMs

TL;DR

Open-Nav investigates using open-source large language models for zero-shot vision-and-language navigation in continuous environments, employing a chain-of-thought reasoning approach to improve navigation performance without relying on proprietary models.

Contribution

This work introduces Open-Nav, a novel approach utilizing open-source LLMs with a spatial-temporal reasoning method for zero-shot VLN in continuous environments, addressing cost and privacy issues.

Findings

Open-Nav achieves competitive results with closed-source LLMs.

The spatial-temporal chain-of-thought improves navigation reasoning.

Open-Nav performs well in both simulated and real-world tests.

Abstract

Vision-and-Language Navigation (VLN) tasks require an agent to follow textual instructions to navigate through 3D environments. Traditional approaches use supervised learning methods, relying heavily on domain-specific datasets to train VLN models. Recent methods try to utilize closed-source large language models (LLMs) like GPT-4 to solve VLN tasks in zero-shot manners, but face challenges related to expensive token costs and potential data breaches in real-world applications. In this work, we introduce Open-Nav, a novel study that explores open-source LLMs for zero-shot VLN in the continuous environment. Open-Nav employs a spatial-temporal chain-of-thought (CoT) reasoning approach to break down tasks into instruction comprehension, progress estimation, and decision-making. It enhances scene perceptions with fine-grained object and spatial knowledge to improve LLM's reasoning in navigation. Our extensive experiments in both simulated and real-world environments demonstrate that Open-Nav achieves competitive performance compared to using closed-source LLMs.