Fast planning of well conditioned trajectories for model learning

This paper discusses the problem of planning well conditioned trajectories for learning a class of nonlinear models such as the imaging model of a camera and the multibody dynamic model of a robot. In such model learning problems, the model parameters can be linearly decoupled from system variables in the feature space. The learning accuracy and robustness against measurement noise and unmodeled response depend largely on the condition number of the data matrix. A new method is proposed to plan well conditioned trajectories efficiently by using low-discrepancy sequences and matrix subset selection. Application examples show promising results.