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Bae, Joonbum
Bio-Robotics and Control (BiRC) Lab
Research Interests
  • Design and control of physical human-robot interaction systems
  • Soft robotics
  • Intelligent interaction algorithms for virtual reality, tele-operation and rehabilitation
  • Bio-inspired robotics for improved mobility

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Review of machine learning methods in soft robotics

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Title
Review of machine learning methods in soft robotics
Author
Kim, DaekyumKim, Sang-HunKim, TaekyoungKang, Brian ByunghyunLee, MinhyukPark, WookeunKu, SubyeongKim, DongWookKwon, JunghanLee, HochangBae, JoonbumPark, Yong-LaeCho, Kyu-JinJo, Sungho
Issue Date
2021-02
Publisher
Public Library of Science
Citation
PLOS ONE, v.16, no.2, pp.e0246102
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/49853
URL
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246102
DOI
10.1371/journal.pone.0246102
ISSN
1932-6203
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