  Model-based paradigms for decision-making and control are becoming ubiquitous
in robotics. They rely on the ability to efficiently learn a model of the
system from data. Structured Mechanical Models (SMMs) are a data-efficient
black-box parameterization of mechanical systems, typically fit to data by
minimizing the error between predicted and observed accelerations or next
states. In this work, we propose a methodology for fitting SMMs to data by
minimizing the discrete Euler-Lagrange residual. To study our methodology, we
fit models to joint-angle time-series from undamped and damped
double-pendulums, studying the quality of learned models fit to data with and
without observation noise. Experiments show that our methodology learns models
that are better in accuracy to those of the conventional schemes for fitting
SMMs. We identify use cases in which our method is a more appropriate
methodology. Source code for reproducing the experiments is available at
https://github.com/sisl/delsmm.
