  Data driven models of dynamical systems help planners and controllers to
provide more precise and accurate motions. Most model learning algorithms will
try to minimize a loss function between the observed data and the model's
predictions. This can be improved using prior knowledge about the task at hand,
which can be encoded in the form of constraints. This turns the unconstrained
model learning problem into a constrained one. These constraints allow models
with finite capacity to focus their expressive power on important aspects of
the system. This can lead to models that are better suited for certain tasks.
This paper introduces the constrained Sufficiently Accurate model learning
approach, provides examples of such problems, and presents a theorem on how
close some approximate solutions can be. The approximate solution quality will
depend on the function parameterization, loss and constraint function
smoothness, and the number of samples in model learning.
