  Model-based controllers on real robots require accurate knowledge of the
system dynamics to perform optimally. For complex dynamics, first-principles
modeling is not sufficiently precise, and data-driven approaches can be
leveraged to learn a statistical model from real experiments. However, the
efficient and effective data collection for such a data-driven system on real
robots is still an open challenge. This paper introduces an optimization
problem formulation to find an informative trajectory that allows for efficient
data collection and model learning. We present a sampling-based method that
computes an approximation of the trajectory that minimizes the prediction
uncertainty of the dynamics model. This trajectory is then executed, collecting
the data to update the learned model. In experiments we demonstrate the
capabilities of our proposed framework when applied to a complex
omnidirectional flying vehicle with tiltable rotors. Using our informative
trajectories results in models which outperform models obtained from
non-informative trajectory by 13.3\% with the same amount of training data.
Furthermore, we show that the model learned from informative trajectories
generalizes better than the one learned from non-informative trajectories,
achieving better tracking performance on different tasks.
