  The field of physical human-robot interaction has dramatically evolved in the
last decades. As a result, the robotic system's requirements have become more
challenging, including personalized behavior for different tasks and users.
Various machine learning techniques have been proposed to give the robot such
adaptability features. This paper proposes a model-based evolutionary
optimization algorithm to tune the apparent impedance of a wrist rehabilitation
device. We used passivity to define boundaries for the possible controller
outcomes, limiting the shared autonomy of the robot and ensuring the coupled
system stability. The experiment consists of a hardware-in-the-loop
optimization and a one-degree-of-freedom robot used for wrist rehabilitation.
Experimental results showed that the proposed technique could generate
customized passive impedance controllers for three subjects. Furthermore, when
compared with a constant impedance controller, the method suggested decreased
in 20\% the root mean square of interaction torques while maintaining stability
during optimization.
