Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning

Weizheng Wang; Le Mao; Ruiqi Wang; Byung-Cheol Min

ICRA 2024poster17 citations

Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning

Weizheng Wang, Le Mao, Ruiqi Wang, Byung-Cheol Min

Abstract

In public spaces shared with humans, ensuring multi-robot systems navigate without collisions while respecting social norms is challenging, particularly with limited communication. Although current robot social navigation techniques leverage advances in reinforcement learning and deep learning, they frequently overlook robot dynamics in simulations, leading to a simulation-to-reality gap. In this paper, we bridge this gap by presenting a new multi-robot social navigation environment crafted using Dec-POSMDP and multi-agent reinforcement learning. Furthermore, we introduce SAMARL: a novel benchmark for cooperative multi-robot social navigation. SAMARL employs a unique spatial-temporal transformer combined with multi-agent reinforcement learning. This approach effectively captures the complex interactions between robots and humans, thus promoting cooperative tendencies in multi-robot systems. Our extensive experiments reveal that SAMARL outperforms existing baseline and ablation models in our designed environment. Demo videos for this work can be found at: https://sites.google.com/view/samarl

BibTeX

@inproceedings{icra2024_multirobotcooper,
  title = {Multi-Robot Cooperative Socially-Aware Navigation Using Multi-Agent Reinforcement Learning},
  author = {Weizheng Wang and Le Mao and Ruiqi Wang and Byung-Cheol Min},
  booktitle = {ICRA 2024},
  year = {2024}
}