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A Survey for Machine Learning-Based Control of Continuum Robots.

Xiaomei Wang1,2, Yingqi Li1, Ka-Wai Kwok1

  • 1Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, SAR China.

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|October 25, 2021
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Summary
This summary is machine-generated.

Soft continuum robots offer safe interaction in biomedical applications. Learning-based control strategies address modeling uncertainties, improving performance in soft robotic manipulators.

Keywords:
continuum robotsdata-driven controlinverse kinematics (IK)kinematic/dynamic model-free controllearning-based controlmachine learningreinforcement learningsoft robots

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Control Systems

Background:

  • Soft continuum robots are promising for biomedical applications due to their inherent compliance, enabling safe interaction.
  • Their application in minimally invasive surgery mirrors conventional endoscopy/laparoscopy but faces challenges.
  • Unlike rigid robots, soft robots exhibit modeling uncertainties affecting model-based control.

Purpose of the Study:

  • To overview current model-free control schemes for continuum manipulators.
  • To focus on learning-based approaches for soft robot control.
  • To discuss similarities, differences, and future trends in soft robot control methods.

Main Methods:

  • Review of existing literature on data-driven modeling and machine learning for soft robots.
  • Analysis of kinematic and dynamic model-free control schemes.
  • Comparative discussion of various learning-based control strategies.

Main Results:

  • Data-driven strategies using machine learning offer a viable solution for soft robot control challenges.
  • Existing control schemes show diverse approaches to managing flexibility and controllability trade-offs.
  • Identified limitations and challenges in current methods pave the way for future research.

Conclusions:

  • Learning-based control is crucial for overcoming modeling uncertainties in soft continuum robots.
  • Further research is needed to optimize the trade-off between flexibility and controllability.
  • The review provides insights into current trends and future perspectives for soft robot control.