Robotics: Science and Systems XVI

Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots

Utku Culha, Sinan Ozgun Demir, Sebastian Trimpe, Metin Sitti


Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent variability during fabrication on the miniature scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of the robot control is additionally crucial for medical operations, as operation environments show large variability and robot materials may degrade or change over time, which would have deteriorating factors on the robot motion and task performance. In this work, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot. We demonstrate adaptation to fabrication variabilities and different walking surfaces by adopting our controller learning system to three robots within a small number of physical experiments.



    AUTHOR    = {Utku Culha AND Sinan Ozgun Demir AND Sebastian Trimpe AND Metin Sitti}, 
    TITLE     = {{Learning of Sub-optimal Gait Controllers for Magnetic Walking Soft Millirobots}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2020}, 
    ADDRESS   = {Corvalis, Oregon, USA}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2020.XVI.070}