Robotics: Science and Systems XVIII

A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles

Hao Ma, Dieter Büchler, Bernhard Schölkopf, Michael Muehlebach

Abstract:

In this work, we propose a new learning-based iterative control (IC) framework that enables a complex soft-robotic arm to track trajectories accurately. Compared to traditional iterative learning control (ILC), which operates on a single fixed reference trajectory, we use a deep learning approach to generalize across various reference trajectories. The resulting nonlinear mapping computes feedforward actions and is used in a two degrees of freedom control design. Our method incorporates prior knowledge about the system dynamics and by learning only feedforward actions, it mitigates the risk of instability. We demonstrate a low sample complexity and an excellent tracking performance in real-world experiments. The experiments are carried out on a custom-made robot arm with four degrees of freedom that is actuated with pneumatic artificial muscles. The experiments include high acceleration and high velocity motion.

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Bibtex:

  
@INPROCEEDINGS{Ma-RSS-22, 
    AUTHOR    = {Hao Ma AND Dieter Büchler AND Bernhard Schölkopf AND Michael Muehlebach}, 
    TITLE     = {{A Learning-based Iterative Control Framework for Controlling a Robot Arm with Pneumatic Artificial Muscles}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2022}, 
    ADDRESS   = {New York City, NY, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2022.XVIII.029} 
}