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Preprint
Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
arXiv (Cornell University)
18 May 2021
Abstract
The problem of decentralized multi-robot target tracking asks for jointly
selecting actions, e.g., motion primitives, for the robots to maximize target
tracking performance with local communications. One major challenge for
practical implementations is to make target tracking approaches scalable for
large-scale problem instances. In this work, we propose a general-purpose
learning architecture toward collaborative target tracking at scale, with
decentralized communications. Particularly, our learning architecture leverages
a graph neural network (GNN) to capture local interactions of the robots and
learns decentralized decision-making for the robots. We train the learning
model by imitating an expert solution and implement the resulting model for
decentralized action selection involving local observations and communications
only. We demonstrate the performance of our GNN-based learning approach in a
scenario of active target tracking with large networks of robots. The
simulation results show our approach nearly matches the tracking performance of
the expert algorithm, and yet runs several orders faster with up to 100 robots.
Moreover, it slightly outperforms a decentralized greedy algorithm but runs
faster (especially with more than 20 robots). The results also exhibit our
approach's generalization capability in previously unseen scenarios, e.g.,
larger environments and larger networks of robots.
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Details
- Title
- Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection
- Creators
- Lifeng Zhou - University of PennsylvaniaVishnu D Sharma - University of Maryland, College ParkQingbiao Li - University of CambridgeAmanda Prorok - University of CambridgeAlejandro Ribeiro - University of PennsylvaniaPratap Tokekar - University of Maryland, College ParkVijay Kumar - University of Pennsylvania
- Publication Details
- arXiv (Cornell University)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991021945879504721