  Dynamic System Identification approaches usually heavily rely on the
evolutionary and gradient-based optimisation techniques to produce optimal
excitation trajectories for determining the physical parameters of robot
platforms. Current optimisation techniques tend to generate single
trajectories. This is expensive, and intractable for longer trajectories, thus
limiting their efficacy for system identification. We propose to tackle this
issue by using multiple shorter cyclic trajectories, which can be generated in
parallel, and subsequently combined together to achieve the same effect as a
longer trajectory. Crucially, we show how to scale this approach even further
by increasing the generation speed and quality of the dataset through the use
of generative adversarial network (GAN) based architectures to produce a large
databases of valid and diverse excitation trajectories. To the best of our
knowledge, this is the first robotics work to explore system identification
with multiple cyclic trajectories and to develop GAN-based techniques for
scaleably producing excitation trajectories that are diverse in both control
parameter and inertial parameter spaces. We show that our approach dramatically
accelerates trajectory optimisation, while simultaneously providing more
accurate system identification than the conventional approach.
