TAGS: Topology-Aware Graph Scheduling of Heterogeneous Robots for Delivery and Collaboration

Heterogeneous robots serve capacity-limited delivery requests and synchronized collaborative tasks by coordinating repeated cargo trips and capability-covering teams over a shared travel network. These operations compete for robot availability: a delivery can delay team formation, while a partial team reserves robots needed for cargo transport. We introduce Topology-Aware Graph Scheduling (TAGS), a unified learning framework that couples delivery progress and partial-team commitments through an event-driven representation of shared robot availability. On this evolving state, a topology-aware entity graph relates available robot resources to task requirements and travel costs, enabling an autoregressive robot–command policy to coordinate cargo dispatch and team formation. We compare TAGS with adapted attention-based learning, mathematical optimization, neighborhood search, and rule-based scheduling on benchmarks with 15, 35, and 50 heterogeneous robots. With 50 robots scheduling 25 delivery and 25 collaborative tasks, best-of-16 TAGS completes every instance and reduces mean makespan by 6.9% and collaborative waiting by 27.1% relative to the respective best full-completion baselines. It retains the lowest mean makespan under zero-shot transfer from 50% to 75% and 100% collaboration; a three-robot demonstration illustrates physical execution.