  This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method
that enables systems with unknown, nonlinear dynamics to autonomously learn to
solve reference tracking tasks. The method iteratively applies an input
trajectory to the unknown dynamics, trains a Gaussian process model based on
the experimental data, and utilizes the model to update the input trajectory
until desired tracking performance is achieved. Unlike existing approaches, the
proposed method determines necessary parameters automatically, i.e., AI-MOLE
works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE
only requires input/output information, but can also exploit available state
information to accelerate learning.
  While other approaches are typically only validated in simulation or on a
single real-world testbed using manually tuned parameters, we present the
unprecedented result of validating the proposed method on three different
real-world robots and a total of nine different reference tracking tasks
without requiring any a priori model information or manual parameter tuning.
Over all systems and tasks, AI-MOLE rapidly learns to track the references
without requiring any manual parameter tuning at all, even if only input/output
information is available.
