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Using First Principles for Deep Learning and Model-Based Control of Soft Robots.

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Summary

This study introduces a hybrid model combining machine learning and physics-based models for soft robot control. This approach enhances performance and repeatability for complex tasks, overcoming limitations of purely data-driven or analytical methods.

Keywords:
data-driven modelingdeep learningdynamicserror modelingmodel predictive controlsoft robots

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Area of Science:

  • Robotics
  • Control Engineering
  • Machine Learning

Background:

  • Soft robots offer unique compliant capabilities but struggle with repeatable task execution due to modeling challenges.
  • Developing accurate analytical dynamic models for soft robots is complex and time-consuming.
  • Deep learning models require extensive data, which may be infeasible and pose generalization risks for control.

Purpose of the Study:

  • To develop a hybrid modeling approach for soft robot control.
  • To improve the performance and reliability of soft robots in complex tasks.
  • To address the limitations of purely analytical or empirical modeling methods.

Main Methods:

  • Proposed a hybrid modeling strategy integrating machine learning with a first-principles dynamic model.
  • Implemented a sampling-based non-linear model predictive controller utilizing the hybrid model.
  • Validated the approach on a physical soft robot platform.

Main Results:

  • The hybrid model significantly improved control performance compared to existing methods.
  • Demonstrated an average performance enhancement of 52% using the combined model.
  • Showcased the feasibility of hybrid modeling for enhancing soft robot control.

Conclusions:

  • Hybrid modeling offers a robust solution for accurate soft robot dynamics.
  • This approach enhances the repeatability and task-completion capabilities of soft robots.
  • The proposed method mitigates risks associated with purely empirical models in control applications.