Identification of human control law during physical Human-Robot Interaction

With the growing interest in applications involving humans and robots teaming together, the need to understand each other's intentions and behavior arises. This work presents a method to online identify the time-varying human feedback control law during physical Human-Robot Interaction. The robot motion is implemented as a Cartesian impedance, and the interaction with the human happens by force exchange. The coupled system is modeled with a state-space formulation. The state vector is augmented with the unknown parameters, and an Extended Kalman Filter (EKF) is implemented for online identification. This approach is compared with the Least Squares (LS) and the Recursive Least Squares (RLS) methods. Both simulation and experimental results are provided, showing the presented approach's feasibility in identifying the parameters and reconstructing the control inputs.