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
7 Pith papers cite this work. Polarity classification is still indexing.
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WorldFly integrates a world model into a VLA framework via dual-branch coupled flow matching to jointly generate future videos and actions, outperforming baselines on an urban canyon traversal benchmark especially in unseen environments.
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
PEACE decouples single-pass LLM planning from PX4 execution via ROS 2 and a constraint layer, with modular 3D perception, and shows feasibility in Gazebo SITL with improved explainability and fewer LLM calls.
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.
citing papers explorer
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Aero-World: Action-Conditioned Aerial Video Generation from Inertial Controls
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
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WorldFly: A World-Model-Based Vision-Language-Action Model for UAV Navigation
WorldFly integrates a world model into a VLA framework via dual-branch coupled flow matching to jointly generate future videos and actions, outperforming baselines on an urban canyon traversal benchmark especially in unseen environments.
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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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PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs
PEACE decouples single-pass LLM planning from PX4 execution via ROS 2 and a constraint layer, with modular 3D perception, and shows feasibility in Gazebo SITL with improved explainability and fewer LLM calls.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
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.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
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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.