  In emergency scenarios, mobile robots must navigate like humans, interpreting
stimuli to locate potential victims rapidly without interfering with first
responders. Existing socially-aware navigation algorithms face computational
and adaptability challenges. To overcome these, we propose a solution, MIRACLE
-- an inverse reinforcement and curriculum learning model, that employs
gamified learning to gather stimuli-driven human navigational data. This data
is then used to train a Deep Inverse Maximum Entropy Reinforcement Learning
model, reducing reliance on demonstrator abilities. Testing reveals a low loss
of 2.7717 within a 400-sized environment, signifying human-like response
replication. Current databases lack comprehensive stimuli-driven data,
necessitating our approach. By doing so, we enable robots to navigate emergency
situations with human-like perception, enhancing their life-saving
capabilities.
