Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial constraints

Robot teams are increasingly being deployed in environments, such as manufacturing facilities and warehouses, to save cost and improve productivity. To efficiently coordinate multi-robot teams, fast, high-quality scheduling algorithms are essential to satisfy the temporal and spatial constraints imposed by dynamic task specification and part and robot availability. Traditional solutions include exact methods, which are intractable for large-scale problems, or application-specific heuristics, which require expert domain knowledge to develop. In this paper, we propose a novel heterogeneous graph attention network model, called ScheduleNet, to learn scheduling policies that overcome the limitations of conventional approaches. By introducing robot- and proximity-specific nodes into the simple temporal network encoding temporal constraints, we obtain a heterogeneous graph structure that is nonparametric in the number of tasks, robots and task resources or locations. We show that our model is end-to-end trainable via imitation learning on small-scale problems, and generalizes to large, unseen problems. Empirically, our method outperforms the existing state-of-the-art methods in a variety of testing scenarios involving both homogeneous and heterogeneous robot teams.

Automated summary

In this paper, a novel heterogeneous graph attention network model called ScheduleNet is proposed to learn scheduling policies that overcome the limitations of conventional methods. By introducing robot- and proximity-specific nodes into the simple temporal network, a nonparametric heterogeneous graph structure is obtained. The model is shown to be end-to-end trainable on small-scale problems and generalizes to large, unseen problems, outperforming existing state-of-the-art methods in various testing scenarios involving both homogeneous and heterogeneous robot teams.