Gaussian Process-Driven Predictive Control for Robot Re-Orientation in Interaction Tasks

To address the issue of proper robot tool orientation in interaction tasks (e.g., for screwing and assembly tasks), this paper proposes a Gaussian Process-driven predictive controller (GPDPC). The GPDPC allows for the online robot tool orientation alignment w.r.t. the main task direction. The proposed method uses the end-effector wrench measurements/estimations only. The GPDPC exploits a Gaussian Process (GP) to model a cost function providing information about the correct alignment of the robot tool w.r.t. the main task direction. The predictive controller then uses the GP online to compute the rotational optimal trajectory for the reorientation of the robot tool. The GP estimated variance is used to guide the robot safely. Derivation of the controller is presented, together with simulation (considering a probing task) and experimental results showing the achieved performance. The GPDPC performance are compared with a state-of-the-art method to show its superiority.