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Abstract The coupling of dynamical systems within living organisms frequently gives rise to emergent synergies, as seen in animal movement and homeostatic control. These natural mechanisms have inspired robotic control strategies, but existing approaches require precise models and significant manual or computational effort to engineer coupling.
Abstract Natural behavior emerging in nonlinear dynamical systems enables reservoir computers to control underactuated robots by approximating their inverse dynamics. Unlike other model-free approaches, the reservoir controllers are sample-efficient, meaning a weighted average of the reservoir output can be trained with a limited amount of pre-recorded data.
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