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Autonomous underwater vehicles can now navigate complex environments more effectively by combining the reasoning of vision-language models with dynamics-aware planning, achieving better coverage and zero collisions.
By predicting parameters for classical planners, APPLV offers a way to leverage foundation models for robot navigation while retaining safety guarantees and outperforming end-to-end VLA models.
