{"id":468,"date":"2024-06-28T06:53:59","date_gmt":"2024-06-28T06:53:59","guid":{"rendered":"https:\/\/ieee-cyber.org\/2024\/?page_id=468"},"modified":"2024-07-14T11:12:32","modified_gmt":"2024-07-14T11:12:32","slug":"plenary-talks","status":"publish","type":"page","link":"https:\/\/ieee-cyber.org\/2024\/plenary-talks\/","title":{"rendered":"Plenary Talks"},"content":{"rendered":"\t\t
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Plenary Talks<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Professor Abderrahmane Kheddar<\/strong><\/p>

CNRS Professor
CNRS-AIST Joint Robotics Laboratory, IRL3218, Japan
CNRS-University of Montpellier LIRMM, UMR5506, France
Member of the National Academy of Technology of France
IEEE Fellow AAIA Fellow
International Artificial Intelligence Industry Alliance (AIIA), Vice-Chairman
Knight in the National Order of the Merit of France<\/p>

Humanoid robots (humanoids) are rapidly gaining maturity in terms of hardware and embedded software. Recently, we are witnessing substantial improvement of existing series and the revelation of new platforms. So far, humanoids’ developments have been confined to research, and there is still much to do in several specific aspects in order to engage into an innovation pathway in cybernetic integration into the many shades of the digital society. In particular, we will discuss the integration of humanoid robots in the manufacturing sector, the social and medical sectors such as assistance for frail persons, and the possible deployment in future cities as physical cybernetic avatars. Other perspectives in science, implying advances in machine intelligence and related bionics are also evoked.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Cybernetic Humanoids<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Professor Abderrahmane Kheddar<\/strong> received the B.S. degree in computer science from the Institut National d\u2019Informatique, Algiers, Algeria, in 1990, and the M.Sc. and Ph.D. degrees in robotics from Pierre et Marie Curie University, Sorbonne University, Paris, France in 1993 and 1997, respectively. In 2008, he created the CNRS-AIST Joint Robotic Laboratory, an International Research Laboratory, located in Tsukuba, Japan, where he was the Director from 2008 to 2016 and Codirector from 2017 to 2021. In 2010 he also created and led the Interactive Digital Humans team until 2020, with the Laboratory of Computer Science, Robotics and Microelectronics of Montpellier, CNRS, University of Montpellier, France. His research interests include haptics, humanoids, and related bionics. He is a Founding Member of the IEEE Robotics and Automation Society (RAS) Chapter on Haptics, and the Co-Chair and Founding Member of the IEEE RAS Technical Committee on Model-Based Optimization. He is a Member of the Steering Committee of the IEEE Brain Initiative, an Editor of the IEEE Robotics and Automation Letters, and a Founding Member and the Deputy Editor-in-Chief for Cyborg and Bionics System. He was an Editor of the IEEE Transactions on Robotics, from 2013 to 2018. He is a Founding Member of the IEEE Transactions on Haptics and was in its Editorial Board from 2007 to 2010. Since 2020 he is the lead of the bionics initiative at CARTIGEN, University Hospital of Montpellier. He is a Fellow of the IEEE, a Fellow of the Asia-Pacific Artificial Intelligence Association and Vice-President of the International Artificial Intelligence Industry Alliance (AIIA). He is a Full Member of the National Academy of Technology of France and a Knight of the National Order of Merits of France.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Jing Xu<\/span>\u00a0is a tenured associate professor at Tsinghua University. He is leader of the Institute of Mechanical Electronics Engineering in the Department of Mechanical Engineering. He\u00a0received\u00a0the\u00a0B.E. degree in mechanical engineering from Harbin Institute of Technology,\u00a0Harbin, China, in 2003, and the Ph.D. degree in mechanical engineering for Tsinghua University, Beijing, China, in 2008. He was a Postdoctoral Researcher in Department of Electrical and Computer Engineering, Michigan State University, East Lansing. He was selected into the National Youth Talent Program and mainly engaged in research on intelligent robot and smart manufacturing. He has undertaken dozens of projects such as key projects of the National Natural Science Foundation of China, key projects of the Ministry of Science and Technology, key projects of the Beijing Natural Science Foundation, etc. He has published more than 90 journal papers, including IEEE TRO, IEEE TPAMI, Science Advance, etc. His publication has received over 5,000 citations, including several highly cited paper awards in Web of Science. He has authorized more than 60 invention patents. He won first prizes for National Teaching Achievement Award for Higher Education (undergraduate), Beijing Education and Teaching Achievement Award, Sichuan Provincial Scientific and Technological Progress Award and China Machinery Industry Science and Technology Award,\u00a0China Instrument and Control Society Technological Invention Award. He has also won Special Award of 18th Beijing Invention and Innovation Competition, Gold Medal at National Exhibition of Inventions, and multiple best conference papers at international conferences. He also served as Associate editor of Robotica.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The intelligent robotic manipulation<\/strong><\/p>

Abstract: The robotic manipulation has been widely used in structured environment, such as automotive manufacturing industry; however, the contact-rich robotic manipulation in unstructured environment is still challenging due to nature uncertainty. In this talk, we focus on vision perception, simulator and manipulation skill for contact-rich task. Firstly, to improve the reconstruction accuracy and completeness in active stereovision depth sensor, we propose the model-based pattern optimization methods for complex-geometry objects and learning-based reconstruction methods for optically-challenging objects. Secondly, to construct the databases of manipulation skill learning, we propose the accurate simulators with reality level, including a physics-grounded simulator for 6D pose estimation with active stereovision depth sensor and a general-purpose simulator for manipulation skill learning with marker-based visuotactile sensors. Thirdly, to Improve the safety and efficiency of manipulation skill learning, we propose the hierarchical reinforcement learning for robotic contact-rich manipulation; specifically, the parameterized impedance-conditioned action space is proposed for reinforcement learning lower-level policy for contact force control and a linear Gaussian contextual policy is formulated as the higher-level policy for task generalization.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Professor Xiaodong Zhang<\/strong> received the B.E. degree in Energy and Power Engineering from Xi\u2019an Jiaotong University, Xi\u2019an, China, in 1989, and the M.E. and Ph.D. degree in Mechanics from Xi\u2019an Jiaotong University, Xi\u2019an, China, in 1992 and 1996, respectively. From 1996 to 2010, he was a Teacher, and in 1999 promoted as an Associate Professor in school of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China. Since 2011 he has been a Professor, and now is a deputy director, Institute of Robotics and Intelligent Systems with school of Mechanical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China.<\/p>

Professor Xiaodong Zhang was a Visiting Scholar in Department of Mechanical and Medical Engineering, University of Bradford, UK, about three months from 2001 to 2002. He was a Researcher in School of Mechanical Engineering, Sungkyunkwan University, Korea, about two years from 2003 to 2005. He was a visiting professor in the Lab on Intelligent robotics, Michigan State University, USA, about one month in November, 2010, and in the Lab on the Control and Intelligent Systems Engineering, University of Hull, UK, about 3 weeks in October, 2011, and in School of Industrial and Systems Engineering, Georgia Institute of Technology, USA, about 2 weeks in July, 2017, and in the Lab on Mechatronics and Intelligent robotics, University of Essex, UK, about 6 months from 2019 to 2020, respectively. Furthermore, he was a lecture professor funded by K. C. Wong Education Foundation in Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, about 2 weeks in December, 2018.<\/p>

Professor Xiaodong Zhang has published more than 120 papers, include 50 papers indexed by SCI & about 60 papers indexed by EI, and won 30 Patents of invention & 10 Design Patents, and served as an Associate Editor of Journal of Mechanical Science and Technology & Editor of Intelligent Service Robotics. He won best paper award of the 2008 International Conference on Rehabilitation in China, best session paper award of CCCT 2010 in USA, best application paper award of IEEE URAI 2016 in China, outstanding paper award of IEEE URAI 2017 in Korea, finalist best paper award of IEEE CYBER 2019 in China, best student paper award of IEEE ICMA 2021 in Japan, respectively. He served the IEEE NEMS 2015 as the local arrangement chair, the IEEE URAI 2016 & URAI 2017 as a program co-chair, and the IEEE CYBER 2020 & CIVEMSA 2024 as the General Chair. His recent research interests are Robotics and Intelligent System, Bio-mechatronics technology, EEG\/EMG signal processing and its application on robotics, intelligent measurement and control technology, etc.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Research <\/strong>on<\/b><\/strong>\u00a0<\/b><\/strong>the <\/b><\/strong>Key Technologies<\/b><\/strong>\u00a0<\/b><\/strong>of <\/b><\/strong>L<\/b><\/strong>ower <\/b><\/strong>L<\/b><\/strong>imb <\/b><\/strong>R<\/b><\/strong>ehabilitation <\/b><\/strong>T<\/b><\/strong>raining <\/b><\/strong>R<\/b><\/strong>obot<\/b><\/strong><\/p>

With the development of robot technologies, traditional human assisted rehabilitation\u00a0training\u00a0has been gradually replaced by robot assisted rehabilitation training which has the advantages of movement controllable,\u00a0high repetition, high training density\u00a0and so on. However, due to the lack of an\u00a0effective human-machine interaction interface as well as an\u00a0interactive control technique, the rehabilitation robot\u00a0which can provide a safe, comfortable\u00a0and natural training environment\u00a0is fewer. In addition, how to improve the patients\u2019\u00a0active\u00a0participation\u00a0in training process\u00a0is always a technical problem\u00a0and need to be solved urgently.<\/p>

The bioelectric-based human-machine interface established a direct communication and control channel between the human and the collaborative object\u00a0(robot,\u00a0et al.) as well as the environment\u00a0and it provides new ideas for the study of human-machine interaction and interactive control of lower limb rehabilitation training robot. In order to improve the human-machine interaction performance and achieve\u00a0synchronous\u00a0active\u00a0body weight supported\u00a0treadmill training, surface EMG based fine movement perception and interactive control methods are presented in this talk as follows.<\/p>