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.

翻译：在紧急场景中，移动机器人需像人类一样导航，通过解读刺激信号快速定位潜在受害者，同时避免干扰急救人员。现有社交感知导航算法面临计算能力与适应性挑战。为此，我们提出MIRACLE解决方案——一种结合逆向强化学习与课程学习的模型，通过游戏化学习采集刺激驱动的导航数据，进而训练深度逆最大熵强化学习模型，降低对演示者能力的依赖。测试表明，在400尺寸环境中损失值低至2.7717，实现了对人类响应模式的复现。当前数据库缺乏完备的刺激驱动数据，故需采用本方法。通过该方案，机器人能够以类人感知能力应对紧急导航场景，提升其救援效能。