Evaluating MEDIRL: A Replication and Ablation Study of Maximum Entropy Deep Inverse Reinforcement Learning for Human Social Navigation
On the map: Offline RL and imitation learning from data → Social robot navigation
Abstract (arXiv)
In this study, we enhance the Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework, targeting its application in human robot interaction (HRI) for modeling pedestrian behavior in crowded environments. Our work is grounded in the pioneering research by Fahad, Chen, and Guo, and aims to elevate MEDIRL's efficacy in real world HRI settings. We replicated the original MEDIRL model and conducted detailed ablation studies, focusing on key model components like learning rates, state dimensions, and network layers. Our findings reveal the effectiveness of a two dimensional state representation over three dimensional approach, significantly improving model accuracy for pedestrian behavior prediction in HRI scenarios. These results not only demonstrate MEDIRL's enhanced performance but also offer valuable insights for future HRI system development, emphasizing the importance of model customization to specific environmental contexts. Our research contributes to advancing the field of socially intelligent navigation systems, promoting more intuitive and safer human robot interactions.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Social robot navigation, one of 13 directions in Offline RL and imitation learning from data. Builds on 4 of its 19 references that are in the map. First posted on arXiv 3 Jun 2024.
Builds on
4 of 19 references.
References · 4
- 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
- 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 173
- 2019Ablation Studies in Artificial Neural Networkscited by 58
- 2021Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learningcited by 9
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- 2024Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
- 2019MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning
- 2021Sample Efficient Social Navigation Using Inverse Reinforcement Learning
- 2018Comparative Analysis of Human Movement Prediction: Space Syntax and Inverse Reinforcement Learning
- 2020NaviGAN: A Generative Approach for Socially Compliant Navigation
- 2026Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling