M. Zeilinger

A DMPC framework in which each agent is assigned a connectivity contract: a local region prescribing where its predicted positions may lie over the prediction horizon, and it is proved that the resulting closed-loop system maintains connectivity, avoids collisions, and respects local state and input constraints.

Safe visuomotor control from high-resolution images is now practical at scale, thanks to a learned visual abstraction coupled with an efficient SLS solver.

Guaranteeing safety in multi-agent systems with dynamic network topologies is now possible with a plug-and-play protocol that allows agents to join or leave the network while ensuring safety recovery.

Achieve real-time online learning for model predictive control with a novel spatio-temporal Gaussian Process approximation that maintains constant computational complexity.