AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild

TL;DR

AutoFly is an innovative vision-language-action model enabling UAVs to autonomously navigate complex outdoor environments by integrating depth perception, continuous planning, and obstacle avoidance, outperforming existing baselines.

Contribution

The paper introduces AutoFly, a novel end-to-end VLA model with a pseudo-depth encoder and a new real-world autonomous navigation dataset, advancing UAV autonomous navigation in unstructured environments.

Findings

AutoFly achieves 3.9% higher success rate than state-of-the-art baselines.

The model performs consistently across simulated and real-world environments.

The new dataset emphasizes autonomous planning and obstacle avoidance.

Abstract

Vision-language navigation (VLN) requires intelligent agents to navigate environments by interpreting linguistic instructions alongside visual observations, serving as a cornerstone task in Embodied AI. Current VLN research for unmanned aerial vehicles (UAVs) relies on detailed, pre-specified instructions to guide the UAV along predetermined routes. However, real-world outdoor exploration typically occurs in unknown environments where detailed navigation instructions are unavailable. Instead, only coarse-grained positional or directional guidance can be provided, requiring UAVs to autonomously navigate through continuous planning and obstacle avoidance. To bridge this gap, we propose AutoFly, an end-to-end Vision-Language-Action (VLA) model for autonomous UAV navigation. AutoFly incorporates a pseudo-depth encoder that derives depth-aware features from RGB inputs to enhance spatial reasoning, coupled with a progressive two-stage training strategy that effectively aligns visual, depth, and linguistic representations with action policies. Moreover, existing VLN datasets have fundamental limitations for real-world autonomous navigation, stemming from their heavy reliance on explicit instruction-following over autonomous decision-making and insufficient real-world data. To address these issues, we construct a novel autonomous navigation dataset that shifts the paradigm from instruction-following to autonomous behavior modeling through: (1) trajectory collection emphasizing continuous obstacle avoidance, autonomous planning, and recognition workflows; (2) comprehensive real-world data integration. Experimental results demonstrate that AutoFly achieves a 3.9% higher success rate compared to state-of-the-art VLA baselines, with consistent performance across simulated and real environments.