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

arXiv: https://arxiv.org/abs/2312.03651 · HTML: https://mlsanity.com/paper/2312.03651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Reinforcement Learning Approaches in Social Robotics](https://mlsanity.com/paper/2009.09689/) (2020)
- [From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation](https://mlsanity.com/paper/2101.02647/) (2021)
- [Socially-Aware Navigation: A Non-linear Multi-Objective Optimization Approach](https://mlsanity.com/paper/1911.04037/) (2019)
- [DeFINE: Delayed Feedback based Immersive Navigation Environment for Studying Goal-Directed Human Navigation](https://mlsanity.com/paper/2003.03133/) (2020)
- [Data Games: A Game-Theoretic Approach to Swarm Robotic Data Collection](https://mlsanity.com/paper/2303.03602/) (2023)
- [Evolutionary Curriculum Training for DRL-Based Navigation Systems](https://mlsanity.com/paper/2306.08870/) (2023)

## Built on by (most cited first, 1 of 1)

- [Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning](https://mlsanity.com/paper/2412.19159/) (2024)

## Abstract (arXiv)

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.