Gradient Descent-Based Task-Orientation Robot Control Enhanced With Gaussian Process Predictions

This letter proposes a novel force-based task-orientation controller for interaction tasks with environmental orientation uncertainties. The main aim of the controller is to align the robot tool along the main task direction (e.g., along screwing, insertion, polishing, etc.) without the use of any external sensors (e.g., vision systems), relying only on end-effector wrench measurements/estimations. We propose a gradient descent-based orientation controller, enhancing its performance with the orientation predictions provided by a Gaussian Process model. Derivation of the controller is presented, together with simulation results (considering a probing task) and experimental results involving various re-orientation scenarios, i.e., i) a task with the robot in interaction with a soft environment, ii) a task with the robot in interaction with a stiff and inclined environment, and iii) a task to enable the assembly of a gear into its shaft. The proposed controller is compared against a state-of-the-art approach, highlighting its ability to re-orient the robot tool even in complex tasks (where the state-of-the-art method fails).