  Accurate dynamic models are crucial for many robotic applications.
Traditional approaches to deriving these models are based on the application of
Lagrangian or Newtonian mechanics. Although these methods provide a good
insight into the physical behaviour of the system, they rely on the exact
knowledge of parameters such as inertia, friction and joint flexibility. In
addition, the system is often affected by uncertain and nonlinear effects, such
as saturation and dead zones, which can be difficult to model. A popular
alternative is the application of Machine Learning (ML) techniques - e.g.,
Neural Networks (NNs) - in the context of a "black-box" methodology. This paper
reports on our experience with this approach for a real-life 6 degrees of
freedom (DoF) manipulator. Specifically, we considered several NN
architectures: single NN, multiple NNs, and cascade NN. We compared the
performance of the system by using different policies for selecting the NN
hyperparameters. Our experiments reveal that the best accuracy and performance
are obtained by a cascade NN, in which we encode our prior physical knowledge
about the dependencies between joints, complemented by an appropriate
optimisation of the hyperparameters.
