Reliable and Efficient Multi-Agent Coordination via Graph Neural Network
  Variational Autoencoders
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
67c9d3c531e28a674096d786
Yue Meng
claimed_verified
2025-03-06T19:50:38.026Z
668e100b97171f3399e07f5d
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pgzatAZut-uoXOBOwgiFB.jpeg
Yue Meng
user
yuemithucsd
67c9d3c531e28a674096d787
Nathalie Majcherczyk
67c9d3c531e28a674096d788
Wenliang Liu
67c9d3c531e28a674096d789
Scott Kiesel
67c9d3c531e28a674096d78a
Chuchu Fan
67c9d3c531e28a674096d78b
Federico Pecora
Reliable and Efficient Multi-Agent Coordination via Graph Neural Network
  Variational Autoencoders
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
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.
Graph Neural Network Variational Autoencoders GNN-VAE multi-agent coordination centralized optimization Mixed-Integer Linear Program MILP latent space inference feasible proposal performance index