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David A Haggerty1, Michael J Banks1, Ervin Kamenar1,2

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This study introduces a rapid, data-driven method for modeling and controlling soft robots, enabling faster and more dynamic movements than previously possible. The approach uses Koopman operator theory for efficient control of complex, nonlinear robotic systems.

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Area of Science:

  • Robotics
  • Control Theory
  • Machine Learning

Background:

  • Soft robots offer enhanced safety and capabilities in human-centric and complex environments.
  • Modeling and controlling soft robots is challenging due to infinite degrees of freedom and nonlinear dynamics.
  • Existing methods often limit soft robot control to quasi-static motions or quasi-linear deflections.

Purpose of the Study:

  • To advance the modeling and control of soft robots into the inertial and nonlinear dynamic regime.
  • To develop a rapid, data-driven approach for accurate soft robot modeling and control.
  • To enable high-speed and high-deflection movements in soft robotic systems.

Main Methods:

  • Leveraged Koopman operator theory for a data-driven modeling approach.
  • Introduced the static Koopman operator as a pregain term in optimal control.
  • Trained and controlled two morphologically different soft robots.

Main Results:

  • Achieved control of soft robot motions with velocities 10x greater and accelerations 40x greater than prior work.
  • Successfully controlled high-deflection shapes exceeding 110° of curvature.
  • Demonstrated rapid model training (<5 min) and low computational cost for model building (0.5 s).

Conclusions:

  • The developed approach enables rapid modeling and control of soft robots in the inertial, nonlinear regime.
  • This work overcomes limitations of quasi-static and quasi-linear models for soft robots.
  • Paves the way for next-generation compliant, highly dynamic soft robots.