DroneWAM：面向无人机视觉导航的高效世界动作模型

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Abstract:World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicted consequences. For drones, this capability must operate under tight accuracy and efficiency constraints. We present DroneWAM, an efficient world-action model for drone visual navigation. DroneWAM adopts a JEPA-based architecture to model future states directly in representation space, avoiding the cost of explicit future image generation. A pretrained Resampler further compresses dense encoder features into fewer latent tokens, reducing the computation repeated at each imagined step. We also introduce adaptive rollout, where a preference-trained Gate adaptively allocates prediction depth according to the current scene. To support learning under richer aerial motion, we construct DroneNav-6D, a simulated visual navigation dataset with synchronized RGB observations, 6-DoF flight trajectories, control commands, and randomized wind disturbances. On DroneNav-6D, DroneWAM achieves the best trajectory accuracy among the compared methods. Adaptive rollout further reduces the average prediction depth from 8 to 4.58 while improving trajectory accuracy, demonstrating that predictive computation can be allocated more effectively across scenes. \href{this https URL}{Codes and data} will be released.

Subjects:	Computer Vision and Pattern Recognition (cs.CV)
Cite as:	arXiv:2609.33148 [cs.CV]
	(or arXiv:2609.33148v1 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2609.33148 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yijun Shen [view email]
[v1] Sun, 27 Sep 2026 03:21:01 UTC (5,759 KB)