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Decoding Motor Skills of AI and Human Policies: A Study on Humanoid and Human Balance Control

Research output: Contribution to journalArticle

  • Kai Yuan
  • Christopher McGreavy
  • Chuanyu Yang
  • Wouter J. Wolfslag
  • Zhibin Li

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https://ieeexplore.ieee.org/document/9063634
Original languageEnglish
Pages (from-to)87 - 101
Number of pages15
JournalIEEE Robotics and Automation Magazine
Volume27
Issue number2
Early online date13 Apr 2020
DOIs
Publication statusPublished - 1 Jun 2020

Abstract

From the advancement in computers, computer-aided design for mechanical and electronic engineering, architecture and many other engineering fields emerged. Foreseeing a similar development curve and technology wave, we forecast a new emerging discipline in the near future that uses learning-aided approaches for catalysing control development, alongside other similar applications such as in medicine discovery. In this study, we propose a new paradigm of using a machine learning approach to facilitate a quicker, more efficient and effective control development, as a different approach of leveraging the power of machine learning in addition to other options that intent to use learning directly in real-world applications

    Research areas

  • Artificial intelligence, Humanoid robots, Hip, Torso, Training, Control systems

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