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

Utku Culha1, Sinan O Demir1, Sebastian Trimpe2,3

  • 1Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany.

Robotics Science and Systems : Online Proceedings
|March 29, 2021
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Summary

This study introduces a data-efficient learning method for controlling small soft robots. The approach uses Bayesian optimization and Gaussian processes to adapt robot control for surgery and drug delivery applications.

Keywords:
Bayesian optimizationSoft roboticsgait controltransfer learning

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Area of Science:

  • Robotics
  • Soft Robotics
  • Bioengineering

Background:

  • Untethered small-scale soft robots offer potential for minimally invasive surgery and targeted drug delivery.
  • Controlling these robots is challenging due to nonlinear kinematics, fabrication variability, and environmental changes.
  • Adaptive control is essential for reliable performance in dynamic medical environments.

Purpose of the Study:

  • To develop a data-efficient probabilistic learning approach for controlling millimeter-scale magnetic walking soft robots.
  • To enable adaptive control that accounts for fabrication variability and changing surface conditions.
  • To optimize the stride length performance of soft millirobots.

Main Methods:

  • Utilized Bayesian optimization (BO) and Gaussian processes (GPs) for a probabilistic learning framework.
  • Implemented a data-efficient learning scheme requiring minimal physical experiments.
  • Demonstrated adaptation to variations in robot fabrication and walking surface roughness.

Main Results:

  • Successfully adapted controller parameters for three different robots with varying fabrication characteristics.
  • Showcased robot performance adaptation to walking surfaces of differing roughness.
  • Achieved improved learning efficiency through knowledge transfer between robots.

Conclusions:

  • The proposed probabilistic learning approach enables effective and adaptive control of small-scale soft robots.
  • This method addresses key challenges in controlling soft millirobots for delicate applications.
  • The demonstrated adaptability and learning transfer offer a pathway for robust robotic systems in bioengineering and medicine.