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ASGARD proposes a two-phase teacher–student pipeline using reinforcement learning to enhance UAV resilience against action-space attacks, ensuring mission completion through corrected commands and generalization to unseen attacks.
The paper analyzes the gap between teacher mimicry and true task performance in knowledge distillation under teacher misspecification using order-parameter methods, showing that mimicry metrics can be invariant while true errors increase with mismatch.