Abstract Autonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) 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 user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters.

中文翻译：

---

轨迹跟踪应用中的机器人控制参数自动调整

摘要 工业机械手对自主性的要求越来越高。机器人必须能够根据不同的操作条件调整其行为，适应要执行的特定任务，而无需耗费大量时间/资源的人工干预。实现机械手控制参数的自动调整仍然是一项具有挑战性的任务，其中涉及机器人动力学的建模/识别。这通常会导致繁重的过程，无论是在实验时间还是数据处理时间方面。本文解决了用于轨迹跟踪的机械手控制器的自动调整问题，根据要执行的特定轨迹优化控制参数。提出了一种贝叶斯优化算法来调整两个低级控制器参数（即，反馈线性化器和前馈控制器的等效链路质量）和高级控制器参数（即联合 PID 增益）。该算法通过数据驱动程序调整控制参数，优化用户定义的轨迹跟踪成本。还包括确保例如闭环稳定性和最大关节位置误差界限的安全约束。在扭矩控制的 7 自由度 FRANKA Emika 机器人机械手上展示了所提出方法的性能。25 个机器人控制参数（即 4 个链接质量参数和 21 个 PID 增益）在不到 130 次迭代中进行了调整，并且获得了与 FRANKA Emika 嵌入式位置控制器相当的结果。此外，