Gioele Zardini

A unified framework for robot autonomy that blends theory with hands-on practice, making it essential for anyone looking to innovate in autonomous systems.

DIVE redefines node selection in CBS, achieving dive efficiency and early incumbents while slashing memory usage compared to conventional methods.

Confidence can be tuned as a design knob, transforming how we approach uncertainty in neural network processor co-design.

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.