A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies

• URL: http://arxiv.org/abs/2111.01777v1
• Date: Tue, 2 Nov 2021 17:53:54 GMT
• Title: A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
• Authors: Jan Blumenkamp, Steven Morad, Jennifer Gielis, Qingbiao Li, Amanda Prorok
• Abstract summary: 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. We present the design of a system that allows for fully decentralized execution of GNN-based policies.
• Score: 4.40401067183266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 online. https://www.youtube.com/watch?v=COh-WLn4iO4

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