OpenFlyV2: An onboard vision-language-action model for aerial perception and
Overview
This paper introduces OpenFlyV2, a unified onboard vision-language-action model that enables semantic understanding, language-grounded perception, spatial reasoning, and continuous aerial control. The model generates flight waypoints at 10 Hz using an NVIDIA Jetson Orin NX and achieves 48% navigation success in real-world environments without prior aerial training data.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Environment: simulation
“aypoints, while motion-aware temporal memory and action priors reduce the computational cost of closed-loop execution. We construct \dataset{}, a simulation-based dataset comprising approximately 70,000 navigation trajectories and 60,000 vision-language instruction--response pairs. OpenFlyV2 generates action chunks, each comprising 12 waypoints, entirely onboard at 10 Hz using an NVIDIA Jetson Orin NX. It transfers z”
Exact source · revision 1Source owner not reported
Reported quantity · waypoints per action chunk: 12 other · Basis not reported
“aypoints, while motion-aware temporal memory and action priors reduce the computational cost of closed-loop execution. We construct \dataset{}, a simulation-based dataset comprising approximately 70,000 navigation trajectories and 60,000 vision-language instruction--response pairs. OpenFlyV2 generates action chunks, each comprising 12 waypoints, entirely onboard at 10 Hz using an NVIDIA Jetson Orin NX. It transfers z”
Exact source · revision 1Source owner not reported
Reported quantity · action chunk generation rate: 10 other · Basis not reported
“aypoints, while motion-aware temporal memory and action priors reduce the computational cost of closed-loop execution. We construct \dataset{}, a simulation-based dataset comprising approximately 70,000 navigation trajectories and 60,000 vision-language instruction--response pairs. OpenFlyV2 generates action chunks, each comprising 12 waypoints, entirely onboard at 10 Hz using an NVIDIA Jetson Orin NX. It transfers z”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“ero-shot from simulation to physical quadrotor flight, achieving a 48\% navigation success rate across four real-world environments without real-world aerial training data. Extensive results demonstrate that broad vision-language competence and continuous closed-loop flight can coexist within a single model on a resource-constrained aerial platform. Code will be made publicly available at https://github.com/Ideal-ljl”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-10-31T03:58:35.213Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalOpenFlyV2: An onboard vision-language-action model for aerial perception and navigation
This paper introduces OpenFlyV2, a unified onboard vision-language-action model that enables semantic understanding, language-grounded perception, spatial reasoning, and continuous aerial control. The model generates flight waypoints at 10 Hz using an NVIDIA Jetson Orin NX and achieves 48% navigation success in real-world environments without prior aerial training data.
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