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arXiv · 2312.03651

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

Nihal Gunukula·Kshitij Tiwari·Aniket Bera
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Abstract

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.

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Nihal Gunukula, Kshitij Tiwari, Aniket Bera. 2023-12-06. MIRACLE: Inverse Reinforcement and Curriculum Learning Model for Human-inspired Mobile Robot Navigation. https://arxiv.org/abs/2312.03651

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