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Neural controllers for plug-and-play networked systems can now be trained with unconstrained gradient descent while guaranteeing stability, overcoming a fundamental trade-off.
Agents can join or leave a multi-robot network anytime, without losing control or safety, thanks to a novel contract-based approach.