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A hallmark of biological intelligence and control is combinatorialgeneralization: animals are able to learn various things, then piece themtogether in new combinations to produce appropriate outputs for new tasks.Inspired by the ability of primates to readily imitate seen movement sequences,we present a model of motor control using a realistic model of arm dynamics,tasked with imitating a guide that makes arbitrary two-segment drawings. Wehypothesize that modular organization is one of the keys to such flexible andgeneralizable control. We construct a modular control model consisting ofseparate encoding and motor RNNs and a scheduler, which we train end-to-end onthe task. We show that the modular structure allows the model to generalize notonly to unseen two-segment trajectories, but to new drawings consisting of manymore segments than it was trained on, and also allows for rapid adaptation toperturbations. Finally, our model recapitulates experimental observations ofthe preparatory and execution-related processes unfolding during motor control,providing a normative explanation for functional segregation of preparatory andexecution-related activity within the motor cortex.

See and Copy: Generation of complex compositional movements from modular and geometric RNN representations