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Achieve real-time safe control of complex robots by representing their dynamics as a linear system in a higher-dimensional space, enabling fast quadratic programming for both tracking and obstacle avoidance.
Forget trial-and-error: this work provides a theoretical recipe for scaling neural Koopman operators, showing how to optimally allocate effort between data collection and model capacity for robotic control.
