One-Stage Auto-Tuning Procedure of Robot Dynamics and Control Parameters for Trajectory Tracking Applications

Autonomy is increasingly demanded by industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., robot dynamics compensation) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a userdefined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in 125 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved.