NeRF-based image augmentation enables accurate target-specific spacecraft pose estimators to be trained from only 25-400 real images without CAD models or large synthetic datasets.
Towards bridging the space domain gap for satellite pose estimation using event sensing
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QuickLAP fuses LLM-extracted language observations with physical feedback in a closed-form Bayesian update to cut reward learning error by over 70% in a driving simulator and improve user preference in a 15-person study.
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CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations
NeRF-based image augmentation enables accurate target-specific spacecraft pose estimators to be trained from only 25-400 real images without CAD models or large synthetic datasets.
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QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents
QuickLAP fuses LLM-extracted language observations with physical feedback in a closed-form Bayesian update to cut reward learning error by over 70% in a driving simulator and improve user preference in a 15-person study.