UM-HKU teams win multiple first prizes at 2020 World Robot Contest
University of Macau
2021-01-20 16:40
  • (From left) Wang Ze, Liu Xucheng, Wong Chiman, Wan Feng, Shen Lu, and Tao Wei

  • A brain-computer interface experiment

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Two teams formed by the University of Macau (UM) and the University of Hong Kong (HKU) participated in the 2020 World Robot Contest, which is known as the ‘Olympics’ of the robot industry. In the Technical Competition of the Brain-Computer Interfaces (BCI) Controlled Robot Contest / the Fourth China BCI Contest, Team A won the Grand Championship (tied with the team formed by Tsinghua University and Beijing University of Posts and Telecommunications) and the championship in the ERP-based BCI competition, as well as four first prizes in seven individual competitions. Team B won multiple second and third prizes.

This year’s contest attracted teams from many renowned universities, including Tsinghua University, the University of Science and Technology of China, Tianjin University, Xi'an Jiaotong University, Huazhong University of Science and Technology, East China University of Science and Technology, Beijing University of Posts and Telecommunications, Southeast University, Shandong University, Shanghai University, Northwestern Polytechnic University, the Chinese Academy of Military Sciences, the University of China Academy of Science, the Institute of Automation under the China Academy of Science (CAS), the Institute of Semiconductors under the CAS, Hong Kong University of Science and Technology, and Tencent Tianyan Laboratory.

The two teams were supervised by Associate Professor Wan Feng in the Department of Electrical and Computer Engineering, Faculty of Science and Technology, UM, and Associate Professor Hu Yong in Li Ka Shing Faculty of Medicine, HKU. In the same event in 2019, the UM-HKU teams also won the Grand Championship/Grand Prize and established the BCI Tying Record, as well as two championships/first prizes in three individual competitions. The algorithms developed by the teams used advanced machine learning methods and online adaptive optimisation strategies, and the related research results have been published in several prestigious academic journals.

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