Aero-World adapts a pretrained latent diffusion transformer for action-conditioned aerial video generation by injecting inertial action tokens and using a frozen latent-space Physics Probe for inertial consistency supervision during LoRA finetuning, with a new AeroBench benchmark showing improved AA
Racevla: Vla-based racing drone navigation with human-like behaviour
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The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
GRaD-Nav++ combines 3D Gaussian Splatting simulation and differentiable RL to train an onboard VLA policy that achieves 50-83% success on language-guided drone navigation tasks in simulation and real hardware.
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
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GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics
GRaD-Nav++ combines 3D Gaussian Splatting simulation and differentiable RL to train an onboard VLA policy that achieves 50-83% success on language-guided drone navigation tasks in simulation and real hardware.