No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
Merged summary
TL;DR - This paper improves UAV vision-language navigation at inference time by generating and refining multiple flight-plan candidates without retraining the model. Multi-criteria selection targets safer, more accurate, and goal-directed trajectories.
- Generates multiple candidate navigation plans in parallel.
- Uses self-correction to iteratively reassess and refine initial plans.
- Scores candidates on safety, goal alignment, and forward progress.
- Reports state-of-the-art performance using frozen UAV navigation VLMs.
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No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation
TL;DR - This paper improves UAV vision-language navigation at inference time by generating and refining multiple flight-plan candidates without retraining the model. Multi-criteria selection targets safer, more accurate, and goal-directed trajectories.
- Generates multiple candidate navigation plans in parallel.
- Uses self-correction to iteratively reassess and refine initial plans.
- Scores candidates on safety, goal alignment, and forward progress.
- Reports state-of-the-art performance using frozen UAV navigation VLMs.