Gioele Zardini

Optimality guarantees are now possible when jointly optimizing robot design, fleet composition, and task planning for heterogeneous multi-robot systems.

Finally, a rigorous mathematical framework lets you treat deep learning architectures as composable algebraic objects, opening the door to formal verification and automated design.

Guaranteeing completeness in closed-loop multi-agent path finding doesn't require sacrificing scalability: just add certificates to your trajectories.

Bridging the gap between theoretical optimality and practical robustness, ACCBS offers a closed-loop MAPF solution that dynamically adapts its planning horizon for efficient and reliable robot coordination.