Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks

Increasing interest in integrating advanced robotics within manufacturing has spurred a renewed concentration in developing real-time scheduling solutions to coordinate human-robot collaboration in this environment. Traditionally, the problem of scheduling agents to complete tasks with temporal and spatial constraints has been approached either with exact algorithms, which are computationally intractable for large-scale, dynamic coordination, or approximate methods that require domain experts to craft heuristics for each application. We seek to overcome the limitations of these conventional methods by developing a novel graph attention network-based scheduler to automatically learn features of scheduling problems towards generating high-quality solutions. To learn effective policies for combinatorial optimization problems, we combine imitation learning, which makes use of expert demonstration on small problems, with graph neural networks, in a non-parametric framework, to allow for fast, near-optimal scheduling of robot teams with various sizes, while generalizing to large, unseen problems. Experimental results showed that our network-based policy was able to find high-quality solutions for $\sim$90% of the testing problems involving scheduling 2–5 robots and up to 100 tasks, which significantly outperforms prior state-of-the-art, approximate methods. Those results were achieved with affordable computation cost and up to 100× less computation time compared to exact solvers.