Prof. Dr. Gordon Cheng
Academic Career and Research Areas
Professor Cheng (b. 1968) researches the fundamental understanding and construction of cognitive systems. He has made pioneering contributions in Humanoid Robotics, Neuroengineering, Artificial Intelligence for the past 20 years.
Professor Cheng studied information sciences at Wollongong University (Australia) and was awarded a doctorate in systems engineering in 2001 at the department of systems engineering of the Australian National University. He founded the department of humanoid robotics and computational neuroscience at the Institute for Advanced Telecommunications Research in Kyoto (Japan), where he was Department Head from 2003 to 2008. In addition, from 2007 to 2008 he was a project manager at the National Institute of Information and Communications Technology (Japan) and the Japan Science and Technology Agency, where he was responsible for the Computational Brain project (2004-2008). Since 2010, Professor Cheng has been conducting research and teaching at TUM as full professor of cognitive systems. He is coordinator of the Center of Competence Neuro-Engineering in the department of Electrical and Computer Engineering and speaker of the newly established Elite Master of Science program in Neuroengineering (MSNE) of the Elite Network of Bavaria.
- Advanced Robotics, Best Paper Award (2019, 2016)
- IEEE Humanoids, Best Paper Award (2018)
- William Mong Distinguished Award – for AI in the real world: from neuroscience to robotic innovations (2018)
- IEEE Fellow for Contributions in Humanoid Robotic Systems and Neurorobotics (2017)
- IEEE Gennai Medal (2007)
Key Publications (all publications)
G. Cheng, S.K. Ehrlich, M. Lebedev, M.A.L. Nicolelis: “Neuroengineering challenges of fusing robotics and neuroscience”. Science Robotics Vol. 5. 2020; Issue 49, eabd1911.Abstract
G. Cheng, E. Dean-Leon, F. Bergner, J. Rogelio Guadarrama Olvera, Q. Leboutet and P. Mittendorfer, "A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications". Proceedings of the IEEE. 2019.Abstract
G. Cheng, K. Ramirez-Amaro, M. Beetz, Y. Kuniyoshi: ”Purposive learning: Robot reasoning about the meanings of human activities”. Science Robotics. 2019; 4(29): eaav1530.Abstract
A.R.C. Donati, S. Shokur, E. Morya, D.S.F. Campos, R.C.Moioli, C.M. Gitti, P.B. Augusto, S. Tripodi, C.G. Pires, G.A. Pereira, F.L. Brasil, S. Gallo, A.A. Lin, A.K. Takigami, M.A. Aratanha, S. Joshi, H. Bleuler, G. Cheng, A. Rudolph, M.A.L. Nicolelis: “Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients”. Nature Scientific Reports. 2016; 6 (Article number: 30383).Abstract
Karinne Ramirez-Amaro, Michael Beetz, Gordon Cheng: “Transferring Skills to Humanoid Robots by Extracting Semantic Representations from Observations of Human Activities”. Artificial Intelligence Journal, 2015.Abstract