背景：

• 传统的机械臂控制：基于模型的前馈控制器来预测并弥补非线性因素，如重力、科氏力+反馈控制器用于弥补残余误差。
• 缺陷：获得机器人精确模型上存在难度；机器人的物理特性易于发生变化。这些会导致需要对机器人进行系统辨识。

工作：

• addresses the aforementioned problem from the point of view of model-free, learning controller which improves the tracking performance safely in an online fashion.
• Improvement to the control performance is done by feeding an additive compensation signal given by a learned policy function to the robot’s nominal controller.
• Two different compensation methods are proposed in the thesis. Both methods are realized by the so called Actor Critic (AC) algorithm in order to cope with the continuous state of a robot arm. The first method compensates the control input of the nominal controller while the second method modifies the reference trajectory

思路：

• The idea is to first design a stabilizing nominal controller with non-optimal tracking performance.Afterwards, RL is used to learn an additive compensation for the control or reference signal such that the combination ensures a better tracking error. From this point forward, we will call this approach RL-based compensator.