A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
Jan Blumenkamp, Steven Morad, Jennifer Gielis, Qingbiao Li, Amanda Prorok
Abstract
Graph Neural Networks (GNNs) are a paradigm-shifting neural architecture to facilitate the learning of complex multi-agent behaviors. Recent work has demonstrated remarkable performance in tasks such as flocking, multi-agent path planning and cooperative coverage. However, the policies derived through GNN-based learning schemes have not yet been deployed to the real-world on physical multi-robot systems. In this work, we present the design of a system that allows for fully decentralized execution of GNN-based policies. We create a framework based on ROS2 and elaborate its details in this paper. We demonstrate our framework on a case-study that requires tight coordination between robots, and present first-of-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication. A video demonstration of this case-study can be found online11youtube.com/watch?v=COh-WLn4i04.
BibTeX
@inproceedings{icra2022_aframeworkforrea,
title = {A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies},
author = {Jan Blumenkamp and Steven Morad and Jennifer Gielis and Qingbiao Li and Amanda Prorok},
booktitle = {ICRA 2022},
year = {2022}
}