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, as well as the accompanying source code repository, can be found online. https://www.youtube.com/watch?v=COh-WLn4iO4 https://github.com/proroklab/ros2_multi_agent_passage https://github.com/proroklab/rl_multi_agent_passage
@article{arxiv.2111.01777,
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},
journal= {arXiv preprint arXiv:2111.01777},
year = {2022}
}
Comments
Accepted at IEEE ICRA (International Conference on Robotics and Automation) 2022 Final Version (Camera Ready)