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Learning accurate dynamics models is necessary for optimal, compliant controlof robotic systems. Current approaches to white-box modeling using analyticparameterizations, or black-box modeling using neural networks, can suffer fromhigh bias or high variance. We address the need for a flexible, gray-box modelof mechanical systems that can seamlessly incorporate prior knowledge where itis available, and train expressive function approximators where it is not. Wepropose to parameterize a mechanical system using neural networks to model itsLagrangian and the generalized forces that act on it. We test our method on asimulated, actuated double pendulum. We show that our method outperforms anaive, black-box model in terms of data-efficiency, as well as performance inmodel-based reinforcement learning. We also conduct a systematic study of ourmethod's ability to incorporate available prior knowledge about the system toimprove data efficiency.

A General Framework for Structured Learning of Mechanical Systems