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Keynote Speakers

Prof. Juyang Weng
IEEE Life Fellow

Brain-Mind Institute and GENISAMA

Michigan, USA

BIO: Juyang Weng received the BS degree from Fudan University, in 1982, M. Sc. and PhD degrees from the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively, all in computer science. He is a former faculty member of Department of Computer Science and Engineering, faculty member of the Cognitive Science Program, and faculty member of the Neuroscience Program at Michigan State University, East Lansing. He was a visiting professor at the Computer Science School of Fudan University, Nov. 2003 - March 2014. Since the work of Cresceptron (ICCV 1993) the first deep learning neural network for the natural 3D world, he expanded his research interests in biologically inspired systems to developmental learning, including perception, cognition, behaviors, motivation, machine thinking, and conscious learning models. The Developmental learning direction has led to the first conscious learning algorithm. He has published over 300 research articles on related subjects, including task muddiness, intelligence metrics, brain-mind architectures, emergent Turing machines, autonomous programing for general purposes (APFGP), data deletion misconducts via Post-Selection flaws, vision, audition, touch, attention, detection, recognition, autonomous navigation, natural language understanding and machine thinking (e.g., planning). He published with T. S. Huang and N. Ahuja a research monograph titled Motion and Structure from Image Sequences. He authored a book titled Natural and Artificial Intelligence: Computational Introduction to Computational Brain-Mind. Dr. Weng is an Editor-in-Chief of the International Journal of Humanoid Robotics, the Editor-in-Chief of the Brain-Mind Magazine, and an associate editor of the IEEE Transactions on Autonomous Mental Development (now Cognitive and Developmental Systems). With others’ support, he worked on the initiation of the series of International Conference on Development and Learning (ICDL), the IEEE Transactions on Autonomous Mental Development, the Brain-Mind Institute, and the startup GENISAMA LLC. He was an associate editor of the IEEE Transactions on Pattern Recognition and Machine Intelligence and the IEEE Transactions on Image Processing. He is a Life Fellow of IEEE.

 

Speech Title: Conscious Learning and the Challenges in Its Chip Design

Abstract: From a fruit fly to a human, with many animal species in between, do they share a set of biological mechanisms to regulate the lifelong development of the brains?  We have seen very impressive advances in understanding the principles of neuroscience.  However, what is still missing is a conscious learning algorithm that is both broad and deep.  By broad, we mean it approximates such mechanisms across a range of species. By deep, we mean that it specifies sufficient details so that the model can be biologically and computationally verified and corrected across a deep hierarchy of scales, from neurotransmitters, to cells, to brain patterns, to behaviors, to intelligence, to consciousness across the time span of a life.   By algorithm, we mean that it can be fully implemented by a computer or chip programmer.  This talk introduces such an algorithm called Developmental Network 3 (DN-3).  A major extension from the predecessor DN-2 to DN-3 is that the model starts from a single cell inside the skull so that brain patterning is fully automatic in a coarse to fine way, across many tasks in a lifetime.   This biological model has been supported by computational experiments with real sensory data for vision, audition, natural languages, and planning, to be presented during the speech.  The talk will also discuss challenges in its chip design.

Prof. Clément Gosselin
IEEE Fellow, ASME Fellow

Robotics Laboratory
Department of Mechanical Engineering
Université Laval
Québec, Qc, Canada


BIO: Clément Gosselin received the Ph.D. degree from McGill University in Canada and completed a post-doctoral fellowship at INRIA in France.  Since 1989, he has been with the Department of Mechanical Engineering at Université Laval, Québec, Canada where he is a Full Professor since 1997 and where has held a Canada Research Chair from 2001 to 2021. His research interests are kinematics, dynamics and control of robotic mechanical systems with a particular emphasis on the mechanics of grasping, the kinematics and dynamics of parallel manipulators, the development of human-friendly robots and the synthesis of haptic devices.  He is an Editor of the IEEE Robotics and Automation Letters and an Associate Editor of the ASME Journal of Mechanisms and Robotics. Dr. Gosselin received several awards including the ASME DED Mechanisms and Robotics Committee Award in 2008,  the ASME Machine Design Award in 2013 and the IFToMM Award of Merit in 2019. He was appointed Officer of the Order of Canada in 2010 for contributions to research in parallel mechanisms and underactuated systems. He is a fellow of the ASME, of the IEEE and of the Royal Society of Canada.

 

Speech Title: Design and implementation of low-impedance hybrid parallel robots for intuitive physical human-robot interaction
Abstract:
Over the past decades, parallel mechanisms have found applications in many areas including motion simulation, high-speed robots, machine-tools and cable-driven systems, to name a few. More recently, parallel and hybrid robots have been proposed in the emerging field of physical human-robot interaction (pHRI) which aims at taking advantage of the complementary capabilities of robots and humans. One of the key challenges in pHRI is to provide an intuitive physical interaction to the human user. Due to their low moving inertia, parallel robots can be used advantageously to design low-impedance mechanical interfaces in order to increase the mechanical bandwidth of the human-robot interaction, thereby leading to a very intuitive behaviour.  In this presentation, the use of parallel and hybrid robots in the design of pHRI devices is proposed and examples of prototypes developed at Laval University are shown. The use of kinematic redundancy in order to increase the rotational workspace is also discussed. The results clearly demonstrate the capability of parallel mechanisms to provide high interaction bandwidth for pHRI robots.

Prof. Eduardo Nebot
FIEEE (IEEE), FTSE (ATSE)

Emeritus Professor
Patrick Chair in Automation and Logistics
Australian Centre for Field Robotics
School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Australia


BIO:Eduardo Mario Nebot received the Bachelor's degree in Electrical Engineering from the Universidad Nacional del Sur, (Argentina) and MS and PhD degrees from Colorado State University, USA. He is a  Fellow of IEEE and FTSE.
He is a Professor at the University of Sydney in the School of Aerospace, Mechanical and Mechatronic Engineering. He has been appointed as the Patrick Chair of Automatic and logistic in 2004 and he has been the Director of the Australian Centre for Field Robotics from 2011-2020. He is now an Emeritus professor and leading the Intelligent Transport Group at ACFR.
Professor Nebot has a substantial track record in robotics and automation. He has published more than 300 Referee Conference and Journal publications and given a large number of keynotes and industrial presentations. The major impact of his fundamental research is in autonomous system, navigation, mining safety and Intelligent Transport Systems.
Over the past 20 years, he has managed a large number industrial collaboration research projects in the area of Field Robotics. His fundamental research contributions are having a significant impact in the profession.  They are already part of new key autonomous technologies deployed in various industrial environments such as mining, stevedoring, cargo handling and urban road vehicles.  His research group is having an active role in the deployment of new innovative technology in the intelligent transport area involving smart vehicles.

 

Speech Title: Autonomous System in industrial applications and Urban environments
Abstract:
During the last 15 years, we have seen significant progress in many areas related to sensing, navigation, control, planning and machine learning. Fundamental research contributions in these areas have enabled the development and deployment of autonomous system in various domains such as mining, stevedoring, and agriculture to name a few.  This keynote will present the fundamental problems that have been addressed to enable the successful deployment of robotic automation in industrial environments. It will also present current projects in the intelligent transport system (ITS) area and an overview of the fundamental research challenges facing future autonomous applications in more complex scenarios, such as urban vehicle automation.