title: Reliable and Efficient Multi-Agent Coordination via Graph Neural Network
    Variational Autoencoders
summary: Multi-agent coordination is crucial for reliable multi-robot navigation in
  shared spaces such as automated warehouses. In regions of dense robot traffic,
  local coordination methods may fail to find a deadlock-free solution. In these
  scenarios, it is appropriate to let a central unit generate a global schedule
  that decides the passing order of robots. However, the runtime of such
  centralized coordination methods increases significantly with the problem
  scale. In this paper, we propose to leverage Graph Neural Network Variational
  Autoencoders (GNN-VAE) to solve the multi-agent coordination problem at scale
  faster than through centralized optimization. We formulate the coordination
  problem as a graph problem and collect ground truth data using a Mixed-Integer
  Linear Program (MILP) solver. During training, our learning framework encodes
  good quality solutions of the graph problem into a latent space. At inference
  time, solution samples are decoded from the sampled latent variables, and the
  lowest-cost sample is selected for coordination. Finally, the feasible proposal
  with the highest performance index is selected for the deployment. By
  construction, our GNN-VAE framework returns solutions that always respect the
  constraints of the considered coordination problem. Numerical results show that
  our approach trained on small-scale problems can achieve high-quality solutions
  even for large-scale problems with 250 robots, being much faster than other
  baselines. Project page: https://mengyuest.github.io/gnn-vae-coord
paper_authors._id: 67c9d3c531e28a674096d786
paper_authors.name: Yue Meng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T19:50:38.026Z
paper_authors._id: 668e100b97171f3399e07f5d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pgzatAZut-uoXOBOwgiFB.jpeg
paper_authors.fullname: Yue Meng
paper_authors.type: user
paper_authors.user: yuemithucsd
paper_authors._id: 67c9d3c531e28a674096d787
paper_authors.name: Nathalie Majcherczyk
paper_authors._id: 67c9d3c531e28a674096d788
paper_authors.name: Wenliang Liu
paper_authors._id: 67c9d3c531e28a674096d789
paper_authors.name: Scott Kiesel
paper_authors._id: 67c9d3c531e28a674096d78a
paper_authors.name: Chuchu Fan
paper_authors._id: 67c9d3c531e28a674096d78b
paper_authors.name: Federico Pecora
paper_title: Reliable and Efficient Multi-Agent Coordination via Graph Neural Network
    Variational Autoencoders
paper_summary: Multi-agent coordination is crucial for reliable multi-robot navigation in
  shared spaces such as automated warehouses. In regions of dense robot traffic,
  local coordination methods may fail to find a deadlock-free solution. In these
  scenarios, it is appropriate to let a central unit generate a global schedule
  that decides the passing order of robots. However, the runtime of such
  centralized coordination methods increases significantly with the problem
  scale. In this paper, we propose to leverage Graph Neural Network Variational
  Autoencoders (GNN-VAE) to solve the multi-agent coordination problem at scale
  faster than through centralized optimization. We formulate the coordination
  problem as a graph problem and collect ground truth data using a Mixed-Integer
  Linear Program (MILP) solver. During training, our learning framework encodes
  good quality solutions of the graph problem into a latent space. At inference
  time, solution samples are decoded from the sampled latent variables, and the
  lowest-cost sample is selected for coordination. Finally, the feasible proposal
  with the highest performance index is selected for the deployment. By
  construction, our GNN-VAE framework returns solutions that always respect the
  constraints of the considered coordination problem. Numerical results show that
  our approach trained on small-scale problems can achieve high-quality solutions
  even for large-scale problems with 250 robots, being much faster than other
  baselines. Project page: https://mengyuest.github.io/gnn-vae-coord
paper_ai_summary: Graph Neural Network Variational Autoencoders efficiently solve large-scale multi-agent coordination problems by encoding feasible solutions into a latent space for faster inference than centralized methods.
paper_ai_keywords: Graph Neural Network Variational Autoencoders GNN-VAE multi-agent coordination centralized optimization Mixed-Integer Linear Program MILP latent space inference feasible proposal performance index