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Interactive Imitation Learning (IIL) enables agents to acquire desired behaviors through human interventions, but existing methods often place heavy cognitive demands on human supervisors. To address this issue, we introduce the Adaptive Intervention Mechanism (AIM), a novel robot-gated IIL algorithm that learns an adaptive criterion for requesting human demonstrations. AIM leverages a proxy Q-function to model the human intervention rule, dynamically adjusting intervention requests based on the alignment between agent and expert actions. The proxy Q-function assigns high values when the agent deviates from expert behavior and gradually reduces these values as the agent improves, allowing the agent to assess real-time alignment and request assistance only when necessary. Expert-in-the-loop experiments demonstrate that AIM reduces expert monitoring effort by 40% compared to the uncertainty-based baseline Thrifty-DAgger, while improving learning efficiency. Moreover, AIM effectively identifies safety-critical states that warrant expert intervention, leading to higher-quality demonstrations and fewer overall expert data and environment interactions. Code and demo video are available at https://github.com/metadriverse/AIM.