  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.
