Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks (1903.10527v4)

Abstract: We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers that require only local information and communications at test time by imitating the policy of centralized controllers using global information at training time. By extending aggregation graph neural networks to time varying signals and time varying network support, we learn a single common local controller which exploits information from distant teammates using only local communication interchanges. We apply this approach to the problem of flocking to demonstrate performance on communication graphs that change as the robots move. We examine how a decreasing communication radius and faster velocities increase the value of multi-hop information.

• The paper presents a novel GNN-based method that uses local information and imitation learning to replicate centralized decision-making in robot swarms.
• It extends aggregation GNN architectures to manage time-varying signals and dynamic communication networks in large-scale, decentralized systems.
• The approach achieves near-centralized performance in flocking tasks, demonstrating robust scalability and efficient multi-hop communication in swarms.

Analysis of Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks

The paper authored by Tolstaya et al. presents a paper on developing decentralized controllers for swarm robotics using Graph Neural Networks (GNNs). The focus is on controlling large-scale systems of mobile robots that exhibit interacting dynamics while constrained by sparse communication availability. The primary aim is to train local controllers that need only local information at test time while being able to imitate the centralized controllers, which have access to global knowledge during the training phase.

Problem and Methodology

This research tackles the challenge of designing controllers for distributed systems with limited communication capabilities. The complexity of finding optimal decentralized controllers due to such constraints is well-known. The authors proposed leveraging aggregation GNNs to address such challenges, particularly suitable due to their operation based entirely on local exchanges between neighboring agents.

To achieve this, the authors extend existing aggregation GNN architectures to handle time-varying signals and communication networks. The developed GNN enables learning a unified local controller that aggregates information from multi-hop neighbors using local exchanges solely.

Key Contributions

1. Decentralized Controller Design: The work involves designing controllers where agents use local information and multi-hop neighbor information through recursive message-passing facilitated by GNNs.
2. Imitation Learning Approach: By utilizing imitation learning, the paper replicates the decision-making process of centralized controllers using only local actions representative of distributed settings.
3. Scalability in Networked Systems: The proposed method adapts well to changes in the network structure and facilitates training models across varying configurations of agent networks, hence supporting scalability.
4. Application to Flocking: Demonstrating the model through a flocking problem underscores its capability to handle dynamically changing communication networks as the robots move, especially showcasing advantages over conventional local controllers.

Numerical Evaluations and Observations

The results showed that the GNN-based controller attained performance levels nearly matching centralized solutions. Unlike conventional local controllers, the GNN approach handled the dynamic and complex communication scenarios effectively. The need for multi-hop communication was highlighted, especially as the network scenarios shifted with decreasing communication radius and increasing agent velocities.

Implications and Future Work

The paper implies significant potential applications such as swarm robotics in constrained communication environments exemplified by communication-denied regions or environments requiring immediate deployment of sensor networks. The theoretical contributions remain centric around leveraging GNNs to represent policies that include a history of multi-hop neighbor information, thus bolstering decentralized coordination tasks over expansive agent networks.

Looking ahead, future research could extend to robust guarantees under variable mesh size and explore state input constraints to augment its applicability to real-world tasked environments, addressing potential inadequacies seen during sudden changes or unforeseen configuration shifts. Further empirical validation could explore vertical extension into various robotic hardware platforms and simulate real-world environmental factors to test robustness beyond synthetic or idealized settings.

In conclusion, this contribution presents a sophisticated approach to decentralized control in robotic swarms, wherein GNNs effectively bridge the gap between global and limited local information, enhancing cooperation, communication efficiency, and performance reliability across large autonomous systems.