Review of machine learning methods in soft robotics

PLoS One. 2021 Feb 18;16(2):e0246102. doi: 10.1371/journal.pone.0246102. eCollection 2021.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Equipment Design
  • Humans
  • Robotics / instrumentation*
  • Supervised Machine Learning
  • Wearable Electronic Devices

Grant support

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under grant NRF2016R1A5A1938472. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.