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Agents can join or leave a multi-robot network anytime, without losing control or safety, thanks to a novel contract-based approach.
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.
Ditch the conservative assumptions: this new uncertainty bound for kernel regression is distribution-free and scales to multi-output problems, all while playing nice with your existing Gaussian process workflows.
