KEYNOTE SPEAKERS LIST
Fumiya Iida, Institute of Robotics and Intelligent Systems, ETH Zurich, Switzerland
Title: The Next Challenges in Bio-Inspired Robotics
Bruno Siciliano, University of Naples Federico II, Italy
Title: Robots Moving Closer to Humans
Okyay Kaynak, UNESCO Chair on Mechatronics, Bogazici University, Turkey
Title: The Entanglement of Control and IT: Intelligent Control in Mechatronics
Jean-Marc Faure and Jean-Jacques Lesage, Ecole Normale Supérieure de Cachan, France
Title: Improving Dependability of Controlled Systems: A Challenge for Automation Science and Engineering
Kurosh Madani, The University of PARIS-EST Créteil (UPEC), France
Title: Perception & Cognition: Two Foremost Ingredients toward Autonomous Intelligent Robots
Institute of Robotics and Intelligent Systems, ETH Zurich
Fumiya Iida is a SNF professor for bio-inspired robotics at ETH Zurich since August 2009. He received his bachelor and master degrees in mechanical engineering at Tokyo University of Science (Japan, 1999), and Dr. sc. nat. in Informatics at University of Zurich (2006). In 2004 and 2005, he was also engaged in biomechanics research of human locomotion at Locomotion Laboratory, University of Jena (Germany). From 2006 to 2009, he worked as a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology in USA. In 2006, he awarded the Fellowship for Prospective Researchers from the Swiss National Science Foundation, and in 2009, the Swiss National Science Foundation Professorship. His research interest includes biologically inspired robotics, embodied artificial intelligence, and biomechanics, where he was involved in a number of research projects related to dynamic legged locomotion, navigation of autonomous robots, and human-machine interactions.
Nature has been providing a number of inspirations to engineers and scientists in the history of robotics. Many important concepts and theories ranging from mechanical designs to control theories and computational algorithms were developed on the basis of our careful observations and experimentations of biological systems. One of the important lessons from the previous bio-inspired robotics research is that the adequate abstraction of mechanisms in nature gives us substantial impact in the technological development of advanced mechatronic systems. As one of such important design principles originated in recent bio-inspired robotics research, there has been an increasing interest in the use of mechanical dynamics in control of robot behaviors. For example, the mechanical dynamics was nicely exploited to demonstrate many different kinds of animal-like complex behaviors such as walking, running, hopping, swimming, flying, and dancing. There were two important lessons that we learned form this line of research. First, it was identified that the use of mechanical dynamics significantly improves motor control in terms of energetic costs, which is beneficial for mobile robots in particular. And second, the proper designs of mechanical dynamics can provide self-stability to maintain robot trajectories against undesired deviations, which is also known as "mechanical intelligence". Although this approach has shown a number of impressive demonstrations, some additional breakthroughs seem to be necessary toward high-impact applications. In general, one of the fundamental questions is how to scale up the complexity of the systems that rely on passivity-based control. The passivity-based dynamic walking control is, for example, still limited to relatively simple task-environments, and it is still not fully understood how we could scale up. Essentially, when a robot exploits more mechanical dynamics, the system has to suffer from more design and control parameters to be calibrated or optimized in order to achieve sensible motion control. From this perspective, in this talk, we will discuss the next high-priority challenges and potential technological solutions in the field of bio-inspired robotics in order to achieve significant breakthroughs toward practical applications.
University of Naples Federico II
Bruno Siciliano is Professor of Control and Robotics, and Director of the PRISMA Lab in the Department of Computer and Systems Engineering at University of Naples Federico II. His research interests include force and visual control, human-robot interaction, cooperative manipulation and aerial robotics. He has co-authored 7 books, 70 journal papers, 170 conference papers and book chapters. He has delivered 90 invited lectures and seminars at institutions worldwide, and he has been the recipient of several awards. He is a Fellow of IEEE, ASME and IFAC. He is Co-Editor of the Springer Tracts in Advanced Robotics, and of the Springer Handbook of Robotics which received the PROSE Award for Excellence in Physical Sciences & Mathematics. His team is currently involved in six FP7 European projects. Professor Siciliano is the Past-President of the IEEE Robotics and Automation Society.
For more details: http://wpage.unina.it/sicilian
Robots! Robots on Mars and in oceans, in hospitals and homes, in factories and schools, robots fighting fires, making goods and products, saving time and lives. Robots today are making a considerable impact on many aspects of modern life, from manufacturing to healthcare. Reaching for the human frontier, robotics is also vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people’s lives. Unlike the industrial robotics domain where the workspace of machines and humans can be segmented, applications of intelligent machines that work in contact with humans are increasing, which involve e.g. haptic interfaces and teleoperators, cooperative material-handling, power extenders and such high-volume markets as rehabilitation, physical training, entertainment. In this context, it is customary to distinguish between Cognitive Human-Robot Interaction (cHRI) and Physical Human-Robot Interaction (pHRI). This talk is aimed at discussing a number of issues in pHRI concerning with safety, dependability and dexterity. The presentation will be accompanied by videos illustrating experimental tests on both conventional and new lightweight robots endowed with force and vision sensors.
UNESCO Chair on Mechatronics, Bogazici University
Okyay Kaynak received the B.Sc. degree with first class honors and Ph.D. degrees in electronic and electrical engineering from the University of Birmingham, UK, in 1969 and 1972 respectively.
After spending 6 years in industry, he joined the Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey in 1979, where he is presently a Full Professor. He has hold long-term (near to or more than a year) Visiting Professor/Scholar positions at various institutions in Japan, Germany, U.S. and Singapore. His current research interests are in the fields of intelligent control and mechatronics.
Dr. Kaynak is a fellow of IEEE. He has served on many committees of IEEE. He was the president of IEEE Industrial Electronics Society during 2002-2003. Currently he is the Co-Editor-in-Chief of the IEEE Transactions on Industrial Electronics, as well as being the Associate Editor for IEEE Sensors Journal and ASME/IEEEE Transactions on Mechatronics.
This presentation will discuss the challenges that face industry in the 21st century. An assessment of the past will be presented, discussing the profound technological changes that have taken place during the last 2 decades, especially the changes observed in the manufacturing industry. The paradigm change from industrial electronics to industrial informatics will be pointed out to. This will be followed by a look at the evolution of the manufacturing paradigms. In the closing parts of the presentation, the state-of-the-art reached in industrial informatics will be given with examples and a perspective on the future will be presented, pointing out the challenges that the manufacturing industry will have to face by the end of the next decade.
Ecole Normale Supérieure de Cachan
Jean-Marc Faure received the Ph.D. degree from the Ecole Centrale de Paris and the “Habilitation à diriger des recherches” from the University of Aix-Marseille in 1991 and 1997 respectively.
He is currently Professor of Automatic Control at the Institut Supérieur de Mécanique de Paris and researcher at LURPA (Laboratory of Research in Automated Production) of Ecole Normale Supérieure de Cachan, France. His research fields are modeling, synthesis and analysis of Discrete Event Systems (DES) with special focus on formal verification and test methods to improve dependability of critical systems.
J.-M. Faure is member of the IFAC TC 1.3 "Discrete Event and Hybrid Systems" and vice-chair of the TC 5.1 "Manufacturing Plant Control". He is with Jean-Jacques Lesage the initiator of the IFAC Workshops series "Dependable Control of Discrete Systems". He has served in many committees of IFAC and IEEE conferences.
Ecole Normale Supérieure de Cachan
Jean-Jacques Lesage received the Ph.D. degree from the Ecole Centrale de Paris and the “Habilitation à diriger des recherches” from the University Nancy 1 in 1989 and 1994 respectively.
He is currently Professor of Automatic Control at the Ecole Normale Supérieure de Cachan, France, where he was head of the LURPA (Laboratory of Research in Automated Production) during eight years. His research interests are in the areas of formal methods and models of Discrete Event Systems (DES), for modeling, synthesis and analysis. The common objective of his works is to increase the dependability of the DES control.
J.-J. Lesage is member of IEEE and member of IFAC 1.3 TC "Discrete Event and Hybrid Systems". He is with Jean-Marc Faure the initiator of the IFAC Workshops series "Dependable Control of Discrete Systems". He has served in many committees of IFAC and IEEE conferences and has been visiting professor at various institutions in Germany, Portugal and Mexico.
The strong demand from society for safer while more available controlled systems explains that dependability improvement is a very significant concern. It is in particular the case when specifying, designing, implementing and operating transport systems, power production systems, healthcare systems and, generally, any critical system. Field experience in these industrial domains has shown that dependability relies strongly on the part of the system whose behaviour can be described as a discrete event system (DES); this explains why this talk focuses only on this class of models.
Researches in the DES area have yielded many significant theoretical results, as formal synthesis and analysis methods, sound techniques for identification, diagnosis and test; some of these results have been even applied to real automation systems.
However, none of them is able by itself to fully ensure dependability during the whole life-cycle; cooperation between several approaches must therefore been considered to meet this objective. Unfortunately, it is often claimed that this cooperation is difficult or even impossible, because the aims, assumptions, and models which underlie the theoretical contributions are quite different or even opposite. For instance, formal synthesis techniques are aiming to build an a priori correct system which fulfils dependability specifications while the objective of the verification methods is to detect potential errors in a system whose correctness is unknown.
The aim of this keynote is to tackle out this issue by showing first the complementarities of these approaches, then proposing a frame to ease their cooperation. This proposal will be illustrated by several case studies performed in our laboratory in the frame of cooperative research works with industrial partners.
The University of PARIS-EST Créteil (UPEC)
Graduated in fundamental physics in June 1985 from PARIS 7 – Jussieu University. He received his MSc. in Microelectronics and chip architecture from University PARIS 11 (PARIS-SUD), Orsay, France, in September 1986. Received his Ph.D. in Electrical Engineering and Computer Sciences from University PARIS 11 (PARIS-SUD), Orsay, France, in February 1990. In 1995, he received the DHDR Doctor Hab. degree (senior research doctorate degree) from University PARIS 12 – Val de Marne.
He works as Chair Professor in Electrical Engineering of Senart-FB Institute of Technology of University PARIS-EST Creteil (UPEC), France.
From 1992 to 2000 he has been creator and head of DRN (Neural Networks Division) research group. From 2001 to 2004 he has been head of Intelligence in Instrumentation and Systems Laboratory (I2S / JE 2353) of UPEC. Co-creator of Images, Signals and Intelligent Systems Laboratory (LISSI / EA 3956) of UPEC in 2005, head of Intelligent Machines & Systems" research team of LISSI, he is also Vice-director of this laboratory.
He has worked on both digital and analog implementation of massively parallel processors arrays for image processing, electro-optical random number generation, and both analog and digital Artificial Neural Networks (ANN) implementation.
Author and coauthor of more than 250 publications (in international journals, books, conferences' and symposiums' proceedings), he has been regularly invited as key-note and invited lecture by international conferences and symposiums. His current research interests include:
- Complex structures and behaviors modeling,
- Self-organizing, modular and hybrid neural based information processing systems and their real-world and industrial applications,
- Humanoid and collective robotics
- Intelligent fault detection and diagnosis systems.
Since 1996 he is a life-member (elected Academician) of International Informatization Academy. In 1997, he was also elected as permanent Academician (life-member) of International Academy of Technological Cybernetics.
The term “cognition”, refers to the ability for the processing of information applying knowledge. If the word “cognition” has been and continues to be used within quite a large number of different contexts, in the field of computer science, it often intends artificial intellectual activities and processes relating “machines’ awareness” and accomplishment of knowledge-based “intelligent” artificial functions. However, the “awareness” and “knowledge construction” need ability to perceive information from surrounding environment. Thus, “cognition” and “perception” remain inseparable ingredients toward autonomous machines (robots, etc…).
The most of works relating modern robotics, and especially humanoid robots, have concerned either the design of controllers (controlling different devices of such robots in order to maintain equilibrium or to reach desired actions of the components constituting such robots) or the navigation aspects of such robots. In the same way, major part of works dealing with human-like behaviour is connected with abstract tasks, as those relating reasoning inference, interactive deduction, etc…. Inspired by juvenile (early) ages human’s skills developments, especially what concerns human’s early ages walking, we accost the robots’ intelligence, from a different slant directing our attention to both “cognitive” and “perceptual” abilities (skills). Concerning cognitive skill, we construct it on the basis of two kinds of functions: “Unconscious Cognitive Functions” (UCF) and “Conscious Cognitive Functions” (CCF). We identify UCF as activities belonging to the “instinctive” cognition level handling reflexive abilities. Beside this, we distinguish CCF as functions belonging to the “intentional” cognition level handling thought-out abilities. The two above-mentioned kinds of functions have been used as basis of a Multi-level cognitive concept attempting to handle complex artificial behaviours. Regarding perceptual skill, we develop it on the basis of artificial vision and “salient” object detection.
The first key-advantage of conceptualizing the problem within such incline is to detach the build-up of artificial complex behaviour’s modelling from the type of robot: as the early-age human’s abilities development which in its global achievement doesn’t depend on kind of “baby” (early ages human walking development is a typical example). The second chief-benefit of the concept is that the issued artificial structures are “Machine Learning” (Artificial Neural Networks, Fuzzy logic, reinforcement learning, etc…) based, taking advantage from “learning” capacity and “generalization” propensity of such models: allowing a precious potential to deal with high dimensionality, nonlinearity and empirical (non-analytical) proprioceptive or exteroceptive information.