Controlling of compliant-mechanisms with reinforcement learning Driving compliant-mechanisms to target positions is particularly challenging since it is not or hardly possible to set up the inverse kinematics with analytical models. On the basis of an exemplary compliant-mechanism, this work shows how machine learning methods can be applied to successfully learn the corresponding kinematics. This allows statements on how the actuators have to be controlled in order to reach arbitrary points with the mechanism.