Review of machine learning methods in soft robotics.

Journal: PloS one
Published Date: Feb 18, 2021

Abstract

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

Authors

  • Daekyum Kim
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Sang-Hun Kim
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Taekyoung Kim
    Department of Mechanical Engineering, Seoul National University, Seoul 08826, Korea.
  • Brian Byunghyun Kang
    1 Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea.
  • Minhyuk Lee
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Wookeun Park
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Subyeong Ku
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • DongWook Kim
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Junghan Kwon
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Hochang Lee
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Joonbum Bae
  • Yong-Lae Park
    1 Robotics Institute, Carnegie Mellon University , Pittsburgh, Pennsylvania.
  • Kyu-Jin Cho
    Department of Mechanical Engineering, Soft Robotics Research Center, Institute of Advanced Machines and Design (IAMD), The Institute of Engineering Research at Seoul National University, Seoul, Republic of Korea.
  • Sungho Jo
    School of Computing, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea. Electronic address: shjo@kaist.ac.kr.

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