Academic Career and Research Areas
Professor Cheng (b. 1968) researches the fundamental understanding and construction of cognitive systems. He studies ways to combine widely diverse capabilities in multipurpose high-performance robots and develops natural communication mechanisms in order to improve the application friendliness of robots.
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)
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
P. Mittendorfer, E. Yoshida, G. Cheng: “Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot”. Advanced Robotics. 2015; 29(1): 51-67.Abstract