电子科大提出AeroDPO：轻量化无人机自主导航
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TL;DR - AeroDPO is an automated preference-optimization pipeline for lightweight vision-language-action models in autonomous UAV navigation. A 2B model with high-resolution vision achieved a 49.16% success rate on unseen maps while reducing collisions and supporting edge deployment.
- Automatically generates preference pairs by rolling simulations back before collisions, then producing safer corrective actions without human annotation.
- Uses VLM-based filtering to retain 2,005 high-quality samples from 3,855 collision trajectories.
- Experiments indicate visual resolution matters more than model size: a 2B model with 384×768 input matched or exceeded a low-resolution 7B model.
- The INT8 model runs on Jetson Orin NX at 770 ms per inference step and retains a 44.99% success rate on unseen maps.
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电子科大提出AeroDPO：轻量化无人机自主导航
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TL;DR - AeroDPO is an automated preference-optimization pipeline for lightweight vision-language-action models in autonomous UAV navigation. A 2B model with high-resolution vision achieved a 49.16% success rate on unseen maps while reducing collisions and supporting edge deployment.
- Automatically generates preference pairs by rolling simulations back before collisions, then producing safer corrective actions without human annotation.
- Uses VLM-based filtering to retain 2,005 high-quality samples from 3,855 collision trajectories.
- Experiments indicate visual resolution matters more than model size: a 2B model with 384×768 input matched or exceeded a low-resolution 7B model.
- The INT8 model runs on Jetson Orin NX at 770 ms per inference step and retains a 44.99% success rate on unseen maps.