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.

Impedance control is commonly implemented for robotic contact applications. Its performance is a function of the dynamic coupling between the environment and the robot's impedance controller: inertia, stiffness and damping. An interaction task may be considered successfully accomplished when the elected performance criteria, such as rise time, overshoot and accommodation are achieved, all in addition to guaranteeing stability. The selection and online changes of impedance parameters for such purpose is considered challenging for practical applications, given the non modeled dynamics of the robot and its intrinsic impedance, uncertainties in the dynamics of environment and tool. This study proposes the usage of a multi-objective genetic algorithm to obtain enhanced impedance controller gains, with the purpose of providing impedance control adaptability to a robot in a real physical interaction application. In this context, a one-degree-of-freedom mechanical contact model is proposed to obtain the values of the objective function. A genetic algorithm is used to obtain the best gains, as this is a nonlinear task. Simulation and experimental results are presented and discussed validating the system performance, which has shown a convergence of less than 6 generations. Tendencies to convergence of controller parameters were observed and discussed.