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Area of Science:

  • Robotics
  • Machine Learning
  • Control Systems

Background:

  • Biological appendages like octopus arms inspire soft robotic manipulators.
  • Current soft robots lack the dexterity and efficiency of biological systems due to challenges in dynamic control.
  • High-dimensional nonlinear systems in soft robotics hinder traditional model-based control approaches.

Purpose of the Study:

  • To develop a machine learning-based approach for creating dynamic models of soft robotic manipulators.
  • To implement a trajectory optimization method for predictive control in task space.
  • To demonstrate the first learned dynamic model and task space controller for soft robotic manipulators.

Main Methods:

  • Utilized machine learning to develop dynamic models for soft robotic manipulators.
  • Applied trajectory optimization for predictive control in task space.
  • Validated the approach through simulation of an octopus-inspired soft manipulator and experimental testing on a pneumatically actuated soft manipulator.

Main Results:

  • Successfully developed fast and accurate dynamic models for soft robotic manipulators using a machine learning approach.
  • Demonstrated effective predictive control in task space for soft robotic manipulators.
  • Validated the approach on both simulated and real-world soft robotic systems.

Conclusions:

  • The machine learning-based approach shows promise for advancing soft robotic manipulator control.
  • This method enables the development of efficient and dexterous soft robots inspired by biological systems.
  • The approach is applicable to a wide range of soft robotic manipulator designs.