  The use of rehabilitation robotics in clinical applications gains increasing
importance, due to therapeutic benefits and the ability to alleviate
labor-intensive works. However, their practical utility is dependent on the
deployment of appropriate control algorithms, which adapt the level of
task-assistance according to each individual patient's need. Generally, the
required personalization is achieved through manual tuning by clinicians, which
is cumbersome and error-prone. In this work we propose a novel online learning
control architecture, which is able to personalize the control force at run
time to each individual user. To this end, we deploy Gaussian process-based
online learning with previously unseen prediction and update rates. Finally, we
evaluate our method in an experimental user study, where the learning
controller is shown to provide personalized control, while also obtaining safe
interaction forces.
