Learning accurate dynamics models is necessary for optimal, compliant control
of robotic systems. Current approaches to white-box modeling using analytic
parameterizations, or black-box modeling using neural networks, can suffer from
high bias or high variance. We address the need for a flexible, gray-box model
of mechanical systems that can seamlessly incorporate prior knowledge where it
is available, and train expressive function approximators where it is not. We
propose to parameterize a mechanical system using neural networks to model its
Lagrangian and the generalized forces that act on it. We test our method on a
simulated, actuated double pendulum. We show that our method outperforms a
naive, black-box model in terms of data-efficiency, as well as performance in
model-based reinforcement learning. We also conduct a systematic study of our
method's ability to incorporate available prior knowledge about the system to
improve data efficiency.

本文提出了使用神经网络对力学系统的 Lagrangian 以及作用在该系统上的广义力进行建模的方法，说明该方法优于黑匣子模型的数据效率和模型强化学习性能，并进行了系统研究以验证该方法融入先前知识提高了其数据效率。