OpenFlyV2: An onboard vision-language-action model for aerial perception and navigation
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabOpenFlyV2 is a unified onboard vision-language-action model for aerial robots. A shared vision-language representation supports semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within one model. A compact action expert outputs short-horizon waypoints, with temporal memory and action priors reducing closed-loop computation. The authors built a simulation dataset of about 70,000 navigation trajectories and 60,000 instruction-response pairs. The model runs onboard at 10 Hz on an NVIDIA Jetson Orin NX and transfers zero-shot to a quadrotor, reaching a 48% navigation success rate across four real-world environments.
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- Primary sourceHugging Face Daily Papers
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OpenFlyV2 is a unified onboard vision-language-action model for aerial robots. A shared vision-language representation supports semantic understanding, language-grounded perception, spatial reasoning, and continuous flight control within one model. A compact action expert outputs short-horizon waypoints, with temporal memory and action priors reducing closed-loop computation. The authors built a simulation dataset of about 70,000 navigation trajectories and 60,000 instruction-response pairs. The model runs onboard at 10 Hz on an NVIDIA Jetson Orin NX and transfers zero-shot to a quadrotor, reaching a 48% navigation success rate across four real-world environments.
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Source claims not independently checked; the 48% success rate is reported across only four real-world environments.
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Key findings from the stored brief
- The model generates action chunks of 12 waypoints entirely onboard at 10 Hz on a Jetson Orin NX.
- It transfers zero-shot from simulation to physical quadrotor flight without real-world aerial training data.
- It achieves a 48% navigation success rate across four real-world environments.
- The simulation dataset contains about 70,000 navigation trajectories and 60,000 vision-language instruction-response pairs.
Source claims not independently checked; the 48% success rate is reported across only four real-world environments.
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Why this may matter
The work suggests broad vision-language competence and continuous closed-loop flight can coexist in one model on a resource-constrained aerial platform, rather than requiring specialized or offboard systems.
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Source claims not independently checked; the 48% success rate is reported across only four real-world environments.
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