  Modeling how a robot interacts with the environment around it is an important
prerequisite for designing control and planning algorithms. In fact, the
performance of controllers and planners is highly dependent on the quality of
the model. One popular approach is to learn data driven models in order to
compensate for inaccurate physical measurements and to adapt to systems that
evolve over time. In this paper, we investigate a method to regularize model
learning techniques to provide better error characteristics for traditional
control and planning algorithms. This work proposes learning "Sufficiently
Accurate" models of dynamics using a primal-dual method that can explicitly
enforce constraints on the error in pre-defined parts of the state-space. The
result of this method is that the error characteristics of the learned model is
more predictable and can be better utilized by planning and control algorithms.
The characteristics of Sufficiently Accurate models are analyzed through
experiments on a simulated ball paddle system.
