Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Researchers from Fudan University introduce a unified framework for Aerial Vision-and-Language Navigation (VLN) that enables unmanned aerial vehicles (UAVs) to navigate complex urban environments using only egocentric monocular RGB observations and natural language instructions. This approach eliminates the need for costly and complex auxiliary inputs like panoramic images, depth sensors, or odometry. The model formulates navigation as a next-token prediction problem, jointly optimizing spatial perception, trajectory reasoning, and action prediction via prompt-guided multi-task learning. Key innovations include a keyframe selection strategy to reduce visual redundancy and an action merging and label reweighting mechanism to address supervision imbalance. Experiments on the Aerial VLN benchmark demonstrate that the model achieves strong results in RGB-only settings, outperforming existing baselines and significantly closing the performance gap with state-of-the-art panoramic RGB-D methods.

Key takeaway

For Computer Vision Engineers developing lightweight UAV navigation systems, this framework offers a robust solution by operating solely on monocular RGB inputs. You should consider integrating prompt-guided multi-task learning with data preprocessing techniques like action merging and keyframe selection to enhance spatial and temporal reasoning, thereby reducing hardware complexity and improving navigation performance in real-world deployments.

Key insights

A unified framework enables aerial VLN using only monocular RGB and language, outperforming prior RGB-only methods.

Principles

• Monocular RGB is sufficient for robust aerial VLN.
• Multi-task learning improves spatial and temporal reasoning.
• Data preprocessing enhances navigation learning.

Method

The method formulates aerial VLN as next-token prediction, using prompt-guided multi-task learning for spatial perception, trajectory reasoning, and action prediction, enhanced by keyframe selection and action merging.

In practice

• Use keyframe selection to reduce visual redundancy in long trajectories.
• Apply action merging to create semantically clearer motion segments.
• Employ label reweighting to balance action distributions.

Topics

• Aerial Vision-Language Navigation
• Monocular RGB Navigation
• Next-Token Prediction
• Prompt-Guided Multi-Task Learning
• Keyframe Selection

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Robotics Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.