Dimensions overall score 8.0

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• The Factorized Dynamics Transformer (FDT) explicitly factorizes cross-variable coupling and multi-scale temporal effects in AAM dynamics.method 90%
• The Latent Residual Adapter (LRA) enables rapid linear adaptation in latent space via Recursive Least Squares for deployment-time distribution shifts.method 90%
• Conventional analytical dynamic models for AAMs rely on fixed parametric structures and degrade under configuration changes and payload variations.limitation 85%
• The proposed framework demonstrates higher prediction fidelity compared to state-of-the-art learning baselines in real-world experiments with unseen payloads.result 85%
• The framework maintains strict real-time feasibility while providing improved performance.technical 80%
• Existing learning architectures conflate instantaneous inertial dynamics with long-horizon regime evolution in AAMs.limitation 85%
• Aerial Manipulators exhibit nonstationary and multiscale residual dynamics during manipulator reconfiguration and abrupt payload variations.technical 90%
• The adapted residual forecast is directly integrated into a residual-compensated adaptive controller.method 90%

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