Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators

Inverse dynamics models are applied to a plethoraof robot control tasks such as computed-torque control, whichare essential for trajectory execution. The analytical derivationof such dynamics models for robotic manipulators can bechallenging and depends on their physical characteristics. Thispaper proposes a machine learning approach for modelinginverse dynamics and provides information about its implemen-tation on a physical robotic system. The proposed algorithmcan perform online multi-target learning, thus allowing effi-cient implementations on real robots. Our approach has beentested both offline, on datasets captured from three differentrobotic systems and online, on a physical system. The proposedalgorithm exhibits state-of-the-art performance in terms ofgeneralization ability and convergence. Furthermore, it hasbeen implemented within ROS for controlling a Baxter robot.Evaluation results show that its performance is comparable tothe built-in inverse dynamics model of the robot.

 @inproceedings{polydoros2017online,
  title = {Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators},
  pdflink1 = {https://www.researchgate.net/profile/Athanasios_Polydoros/publication/321819351_Online_multi-target_learning_of_inverse_dynamics_models_for_computed-torque_control_of_compliant_manipulators/links/5a34cce4aca27247eddce54d/Online-multi-target-learning-of-inverse-dynamics-models-for-computed-torque-control-of-compliant-manipulators.pdf},
  author = {Polydoros, Athanasios S and Boukas, Evangelos and Nalpantidis, Lazaros},
  booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {4716--4722},
  year = {2017},
  organization = {IEEE},
  public = {yes}
}