Paper Overview
- Research Area: Computer Vision (CV)
- Authors: Yan Deng, Fei Xu
- Published: 2026-08-13
- arXiv: 2508.03419
Summary
Aerial vision-language navigation (VLN) requires an embodied agent to integrate visual evidence over time, plan future actions, and judge when the navigation goal has been reached under partially observable conditions. Although recent Vision-Language-Action (VLA) models offer a promising perception-action paradigm, adapting them to aerial navigation remains challenging due to limited historical context, short planning horizons, and unreliable implicit termination.
To address these issues, this paper proposes DreamFly, a diffusion-based aerial VLN framework built on Dream-VLA.
Key Contributions
1. Causally aligned historical memory: Enhances the current visual representation using only observations from prior decision steps, enabling temporal reasoning without leaking future information. 2. Receding-horizon diffusion planning: The policy predicts a K-step action chunk but executes only the first action before replanning. This "plan K, execute one" strategy uses future actions as auxiliary planning objectives while preserving closed-loop visual feedback. 3. LiteStop termination module: Directly estimates the stopping probability from the action logits at the initial fully-masked state, decoupling explicit termination from action generation.
Experimental Results
Experiments on the OpenFly benchmark show consistent improvements in both seen and unseen environments. DreamFly achieves 32.04% / 29.46% SR and 28.22% / 23.54% SPL on the test-seen / test-unseen splits, outperforming all compared methods on both metrics while achieving the lowest navigation error.
Conclusion
These results demonstrate the effectiveness of jointly modeling historical context, future action structure, and explicit termination for aerial VLN.
--- *Automatically collected on 2026-08-14*