Model-based methods are the dominant paradigm for controlling robotic
systems, though their efficacy depends heavily on the accuracy of the model
used. Deep neural networks have been used to learn models of robot dynamics
from data, but they suffer from data-inefficiency and the difficulty to
incorporate prior knowledge. We introduce Structured Mechanical Models, a
flexible model class for mechanical systems that are data-efficient, easily
amenable to prior knowledge, and easily usable with model-based control
techniques. The goal of this work is to demonstrate the benefits of using
Structured Mechanical Models in lieu of black-box neural networks when modeling
robot dynamics. We demonstrate that they generalize better from limited data
and yield more reliable model-based controllers on a variety of simulated
robotic domains.

本文说明了使用结构化机械模型取代黑盒神经网络在机器人动力学建模上具有的优势，包括数据效率、易于应用先前知识和易于与基于模型的控制技术结合使用。在多个仿真机器人领域中，该方法可以从有限的数据中更好地推广和产生更可靠的基于模型的控制器。