Smart robotics will be a core feature while migrating from Industry 3.0 (i.e., mass manufacturing) to Industry 4.0 (i.e., customized or social manufacturing). A key characteristic of a smart system is its ability to learn. For smart manufacturing, this means incorporating learning capabilities into the current fixed, repetitive, task-oriented industrial manipulators, thus rendering them ‘smart’. In this paper we introduce two reinforcement learning (RL) based compensation methods. The learned correction signal, which compensates for unmodeled aberrations, is added to the existing nominal input with an objective to enhance the control performance. The proposed learning algorithms are evaluated on a 6-DoF industrial robotic manipulator arm to follow different kinds of reference paths, such as square or a circular path, or to track a trajectory on a three dimensional surface. In an extensive experimental study we compare the performance of our learning-based methods with well-known tracking controllers, namely, proportional-derivative (PD), model predictive control (MPC), and iterative learning control (ILC). The experimental results show a considerable performance improvement thanks to our RL-based methods when compared to PD, MPC, and ILC.

In this article we provide experimental results and evaluation of a compensation method which improves the tracking performance of a nominal feedback controller by means of reinforcement learning (RL). The compensator is based on the actor-critic scheme and it adds a correction signal to the nominal control input with the goal to improve the tracking performance using on-line learning. The algorithm has been evaluated on a 6 DOF industrial robot manipulator with the objective to accurately track different types of reference trajectories. An extensive experimental study has shown that the proposed RL-based compensation method significantly improves the performance of the nominal feedback controller.