  Real-world robots are becoming increasingly complex and commonly act in
poorly understood environments where it is extremely challenging to model or
learn their true dynamics. Therefore, it might be desirable to take a
task-specific approach, wherein the focus is on explicitly learning the
dynamics model which achieves the best control performance for the task at
hand, rather than learning the true dynamics. In this work, we use Bayesian
optimization in an active learning framework where a locally linear dynamics
model is learned with the intent of maximizing the control performance, and
used in conjunction with optimal control schemes to efficiently design a
controller for a given task. This model is updated directly based on the
performance observed in experiments on the physical system in an iterative
manner until a desired performance is achieved. We demonstrate the efficacy of
the proposed approach through simulations and real experiments on a quadrotor
testbed.
