分散型GNNポリシーを実機マルチロボットで動かすフレームワーク
A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies
ROS2ベースのフレームワークを構築し、GNNで学習したマルチエージェント方策をアドホック通信による完全分散型の実機ロボット群で初めて実行した。
著者: Jan Blumenkamp, Steven Morad, Jennifer Gielis, Qingbiao Li, Amanda Prorok
分類: cs.RO, cs.LG, cs.MA, cs.SY, eess.SY
原文アブストラクト
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
