Reinforcement learning based compensation methods for robot manipulators

Yudha P. Pane, Subramanya P. Nageshrao, Jens Kober, Robert Babuška

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)
17 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)236-247
JournalEngineering Applications of Artificial Intelligence
Publication statusPublished - 2019

Bibliographical note

Accepted Author Manuscript


  • Actor-critic scheme
  • Reinforcement learning
  • Robotics
  • Tracking control


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