Keynote Speakers

Prof. Jan Peters, IEEE Fellow
Technische Universitat Darmstadt (TU Darmstadt), Germany

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt since 2011, and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI) since 2022. He is also is a founding research faculty member of the Hessian Center for Artificial Intelligence. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society's Early Career Award as well as numerous best paper awards. In 2015, he received an ERC Starting Grant and in 2019, he was appointed IEEE Fellow, in 2020 ELLIS fellow and in 2021 AAIA fellow. Despite being a faculty member at TU Darmstadt only since 2011, Jan Peters has already nurtured a series of outstanding young researchers into successful careers. These include new faculty members at leading universities in the USA, Japan, Germany, Finland and Holland, postdoctoral scholars at top computer science departments (including MIT, CMU, and Berkeley) and young leaders at top AI companies (including Amazon, Boston Dynamics, Google and Facebook/Meta). Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master's degrees in these disciplines as well as a Computer Science PhD from USC. Jan Peters has performed research in Germany at DLR, TU Munich and the Max Planck Institute for Biological Cybernetics (in addition to the institutions above), in Japan at the Advanced Telecommunication Research Center (ATR), at USC and at both NUS and Siemens Advanced Engineering in Singapore. He has led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007-2010) and Intelligent Systems (2010-2021).

Inductive Biases for Robot Learning

Abstract: The recent success of deep supervised learning in computer vision and deep reinforcement learning in simulations and computer games may make it appear as if intelligent robotics was just around the corner. However, these highly successful approaches require giant data sets only available by massive simulation or data collection over the internet. As “real” robots live in “real” time, millions of robots would need to operate similar tasks with similar bodies in similar environments to generate sufficiently large data sets during their life-time to follow this strategy. In this talk, we therefore focus on the important question how induction biases can be used to accelerate data-driven learning of complex robot action policies in the physical world. We show how structured representations can help advance robot learning and that generic domain knowledge be encoded in robot learning algorithms.

Prof. Xingjian Jing, IEEE Senior Member  
City University of Hong Kong, Hong Kong

Xingjian Jing (M’13, SM’17) received the B.S. degree from Zhejiang University, China, the M.S. degree and PhD degree in Robotics from Shenyang Institute of Automation, Chinese Academy of Sciences, respectively. He also achieved the PhD degree in nonlinear systems and signal processing from University of Sheffield, U.K.. He is now a Professor with the Department of Mechanical Engineering, City University of Hong Kong. Before joining in CityU, he was a Research Fellow with the Institute of Sound and Vibration Research, University of Southampton, followed by assistant professor and associate professor with Hong Kong Polytechnic University. His current research interests include: Nonlinear dynamics, Vibration, Control and Robotics, with a series of 200+ publications of 9400+ citations and H-index 50 (in Google Scholar), with a number of patents filed in China and US. He is one of the top 2% highly cited world scientists and a senior IEEE member. Prof Jing is the recipient of a number of academic and professional awards including 2016 IEEE SMC Andrew P. Sage Best Transactions Paper Award, 2017 TechConnect World Innovation Award in US, 2017 EASD Senior Research Prize in Europe, 2017 the First Prize of HK Construction Industry Council Innovation Award, and 2019 HKIE outstand paper award etc. He currently serves Associate Editors of Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Electronics, & IEEE Transactions on Systems, Man, Cybernetics -Systems, and served as Technical Editor of IEEE/ASME Trans. on Mechatronics during 2015-2020. He was the lead editor of a special issue on “Exploring nonlinear benefits in engineering” published in Mechanical Systems and Signal Processing during 2017-2018 and is the lead editor of the other special issue on “Next-generation vibration control exploiting nonlinearities” published in MSSP during 2021-2022.

Complex Nonlinear Systems Identification: A Robust Control/Learning Approach

Abstract: The training problem of feedforward neural networks (FNNs) and identification of nonlinear systems can all be formulated into a robust control problem of a linear discrete dynamic system in terms of the estimation error. The robust control approach greatly facilitates the analysis and design of robust learning algorithms for multiple-input–multiple-output (MIMO) nonlinear systems using various standard robust control methods for addressing different noisy and disturbance issues in data. The drawbacks of some existing learning/identification algorithms can therefore be avoided, and an optimal robust control/learning algorithm can be established. The optimal learning parameters can also be found by utilizing linear matrix inequality optimization techniques. Theoretical analysis and examples including function approximation, system identification, exclusive-or (XOR) and encoder problems are provided to illustrate the results.

Assoc. Prof. Dr. Minh T. Nguyen  
Thai Nguyen University of Technology-Thai Nguyen University, Vietnam

Minh T. Nguyen received his B.S., M.S. and PhD degrees in Electrical Engineering from Hanoi University of Communication and Transport, Hanoi, Vietnam in 2001, Military Technical Academy, Hanoi, Vietnam in 2007, Oklahoma State University, Stillwater, OK, USA, in 2015, respectively. Assoc. Prof. Dr. Minh T. Nguyen is currently the Director of International training and Cooperation center (ITC) at Thai Nguyen University of Technology (TNUT), Viet Nam, and the director of Advanced Wireless Communication Networks (AWCN) Lab. He has interest and expertise in a variety of research topics in telecommunications, computer networking, and signal processing areas, especially compressive sensing, and wireless/mobile sensor/ robotic networks. He serves as technical reviewers for several prestigious journals and international conferences. He also serves as Editors for some journals as, Wireless Communication and Mobile Computing, Transactions on Industrial Networks and Intelligent Systems and Editor in Chief for ICSES Transactions on Computer Networks and Communications. 

Remote Sensing Networks: Technical Problems, Benefits and Challenges

Abstract: Remote sensing has proven to be a viable technology for monitoring and collecting data in a variety of sectors and over a wide range of climatic conditions and locales throughout the last few decades. This talk considers sensing networks that include devices and the networking methods to support different applications. For further details, the talk specifies some networks such as wireless sensor networks, robotic networks (mobile robots/sensors), unmanned aerial vehicles (UAVs) networks. Data collection algorithms in the networks are addressed. Some data processing advanced techniques in the networks are also provided. To navigate the mobile devices in such networks, some control algorithms for mobile agents are considered. Finally, the energy efficient problems for the sensing devices are addressed. The talk will provide some potential points for either future developments or research collaborations.

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