  In this work, we examine a spectrum of hybrid model for the domain of
multi-body robot dynamics. We motivate a computation graph architecture that
embodies the Newton Euler equations, emphasizing the utility of the Lie Algebra
form in translating the dynamical geometry into an efficient computational
structure for learning. We describe the used virtual parameters that enable
unconstrained physical plausible dynamics and the used actuator models. In the
experiments, we define a family of 26 grey-box models and evaluate them for
system identification of the simulated and physical Furuta Pendulum and
Cartpole. The comparison shows that the kinematic parameters, required by
previous white-box system identification methods, can be accurately inferred
from data. Furthermore, we highlight that models with guaranteed bounded energy
of the uncontrolled system generate non-divergent trajectories, while more
general models have no such guarantee, so their performance strongly depends on
the data distribution. Therefore, the main contributions of this work is the
introduction of a white-box model that jointly learns dynamic and kinematics
parameters and can be combined with black-box components. We then provide
extensive empirical evaluation on challenging systems and different datasets
that elucidates the comparative performance of our grey-box architecture with
comparable white- and black-box models.
