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In emergency scenarios, mobile robots must navigate like humans, interpretingstimuli to locate potential victims rapidly without interfering with firstresponders. Existing socially-aware navigation algorithms face computationaland adaptability challenges. To overcome these, we propose a solution, MIRACLE-- an inverse reinforcement and curriculum learning model, that employsgamified learning to gather stimuli-driven human navigational data. This datais then used to train a Deep Inverse Maximum Entropy Reinforcement Learningmodel, reducing reliance on demonstrator abilities. Testing reveals a low lossof 2.7717 within a 400-sized environment, signifying human-like responsereplication. Current databases lack comprehensive stimuli-driven data,necessitating our approach. By doing so, we enable robots to navigate emergencysituations with human-like perception, enhancing their life-savingcapabilities.

MIRACLE: Inverse Reinforcement and Curriculum Learning Model for Human-inspired Mobile Robot Navigation