Tel Aviv, Israel
Dan Halperin received his Ph.D. in Computer Science from Tel Aviv University. He then spent three years at the Computer Science Robotics Laboratory at Stanford University. In 1996 he joined the Department of Computer Science at Tel Aviv University, where he is currently a full professor and for two years was the department chair. Halperin's main field of research is Computational Geometry and Its Applications. Much of his work concerns "geometric arrangements", which are fundamental constructs underlying the algorithmic solution of geometric problems in a wide variety of domains. Application areas he is currently interested in include robotics, automated manufacturing, algorithmic motion planning, and 3D printing. In 2015 he was named an IEEE Fellow. Halperin was the program-committee chair/co-chair of several conferences in computational geometry, algorithms and robotics, including SoCG, ESA, ALENEX, and WAFR. He is on the editorial board of a couple of journals in computational geometry and robotics. A major focus of Halperin's work is in research and development of robust geometric software, in collaboration with a group of European universities and research institutes: the CGAL project and library.
Talk # 1
Multi-Robot Motion Planning: The Easy, the Hard and the Uncharted
Early results in robot motion planning had forecast a bleak future for the field by showing that problems with many degrees of freedom, and in particular those involving fleets of robots, are in tractable. Then came sampling-based planners, which have been successfully, and often easily, solving a large variety of problems with many degrees of freedom. We strive to formally determine what makes a motion-planning problem with many degrees of freedom easy or hard. In the first part of the talk I'll describe our quest to resolve this (still wide open) problem, and some progress we have made in the context of multi-robot motion planning. In the second part of the talk I'll review recent algorithms that we have developed for multi-robot motion planning, which come with near- or asymptotic-optimality guarantees.
Talk # 2
From Piano Movers to Piano Printers: Computing and Using Minkowski Sums
The Minkowski sum of two sets P and Q in Euclidean space is the result of adding every point (position vector) in P to every point in Q. Minkowski sums constitute a fundamental tool in geometric computing, used in a large variety of domains including motion planning, solid modeling, assembly planning, 3d printing and many more. At the same time they are an inexhaustible source of intriguing mathematical and computational problems. We survey results on the structure, complexity, algorithms, and implementation of Minkowski sums in two and three dimensions. We also describe how Minkowski sums are used to solve problems in an array of applications, and primarily in robotics and automation.
David Hsu received BSc in computer science & mathematics from the University of British Columbia, Canada and PhD in computer science from Stanford University, USA. He is currently a professor of computer science at the National University of Singapore (NUS) and the Deputy Director ofNUS Advanced Robotics Center. He is an IEEE Fellow. His recent research focuses on robot planning and learning under uncertainty and human-robot collaboration. He, together with colleagues and students, won the Humanitarian Robotics and Automation Technology Challenge Award at International Conference on Robotics & Automation (ICRA) 2015, the RoboCup Best Paper Award at International Conference on Intelligent Robots & Systems (IROS) 2015, and the Best Systems Paper Award at Robotics: Science & Systems (RSS), 2017. He has chaired or co-chaired several major international robotics conferences, including ICRA 2016, RSS 2015, and International Workshop on the Algorithmic Foundation of Robotics (WAFR) 2004 and 2010. He was an associate editor of IEEE Transactions on Robotics. He is currently an editorial board member of Journal of Artificial Intelligence Research and a member of the RSS Foundation Board.
Talk # 1
Robot Decision-Making under Uncertainty: From Data to Actions
Planning and learning are two primary approaches to intelligent robots. Planning enables us to reason about the consequences of immediate actions far into the future, but it requires accurate world models, which are often difficult to acquire in practice. Policy learning circumvents the need for models and learns a mapping from robot perceptual inputs to actions directly. However, without models, it is much more difficult to generalize and adapt learned policies to new contexts. In this talk, I will present our recent work on robust robot decision-making under uncertainty through planning, through learning, most importantly by integrating planning and learning. I will give several examples, including autonomous vehicle navigation among many pedestrians and human-robot interactive tasks.
Talk # 2
Towards Seamless Human-Robot Collaboration: Intention, Trust, and Adaptation
Early robots often occupied tightly controlled environments, e.g., factory floors, designed to segregate robots and humans for safety. In the near future, robots will "live" with humans, providing a variety of services at homes, in workplaces, or on the road. To become effective and trustworthy collaborators, robots must adapt to human behaviors and more interestingly, adapt to changing human behaviors, as humans adapt as well. I will discuss our recent work on (i) mathematical models for human intentions and trust and (ii) planning and learning algorithms that leverage the model to enable effective robot adaptation to humans. This discussion, I hope, will spur greater interest towards principled approaches that integrate perception, planning, and learning for seamless human-robot collaboration.
Chapel Hill (NC), USA
Ming C. Lin is currently the Elizabeth Stevinson Iribe Chair of Computer Science at the University of Maryland College Park and John R. & Louise S. Parker Distinguished Professor Emerita of Computer Science at the University of North Carolina (UNC), Chapel Hill. She is also an honorary Chair Professor (Yangtze Scholar) at Tsinghua University in China. She obtained her B.S., M.S., and Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley. She received several honors and awards, including the NSF Young Faculty Career Award in 1995, Honda Research Initiation Award in 1997, UNC/IBM Junior Faculty Development Award in 1999, UNC Hettleman Award for Scholarly Achievements in 2003, Beverly W. Long Distinguished Professorship 2007-2010, Carolina Women’s Center Faculty Scholar in 2008, UNC WOWS Scholar 2009-2011, IEEE VGTC Virtual Reality Technical Achievement Award in 2010, and many best paper awards at international conferences. She is a Fellow of ACM, IEEE, and Eurographics.
Talk # 1
From Learning-based Traffic Reconstruction to Autonomous Driving
Rapid urbanization and increasing traffic have led to digitalization of modern cities and automation of transportation means. As new technologies like autonomous systems and self-driving robots emerge, there is an increasing demand to incorporate realistic traffic flows into design of autonomous vehicles. In this talk, we first present a novel method for statistically-based inferencing and traffic visualization using GPS data. This approach reconstruct city-scale traffic using statistical learning on GPS data and metamodel-based simulation optimization for dynamic data completion in areas of insufficient data coverage. Next, we present a unified collision avoidance algorithm for the navigation of arbitrary intelligent agents, from pedestrians to various types of robots, including vehicles. This approach significantly extends our statistically-based WarpDriver algorithm specialized for disc-like agents (e.g. crowds) to a wide array of robots (e.g. autonomous vehicles of different shapes) in a unifying framework using different nonlinear motion extrapolations to support agent dynamics, with additional shapes and soft constraints, to simulate vehicle traffic. Finally, we introduce a learning-based, multi-level control policy for autonomous vehicles by analyzing simulated accident data and using our collision avoidance algorithm, automatic data annotation, and parameterized traffic & vehicle simulation. We conclude by suggesting possible future directions.
Talk # 2
Data-Driven Modeling of Non-Rigid Bodies for Robotics
Non-rigid materials are widely used in medical robotics, design and manufacturing, virtual surgery for soft robot planning, procedural rehearsal and training, etc. Identification of mechanical properties, such as tissue elasticity parameters, is critical to enable medical robots to safely operate within highly unstructured, deformable human bodies and to compute desired, accurate force feedback for individualized haptic display characterized by patient-specific parameters. In addition to medical robots, simulations are also increasingly used for rapid prototyping of clinical devices, pre-operation planning of medical procedures, virtual training exercises for surgeons and supporting personnel, etc. And, bio-tissue elasticity properties are central to developing realistic and predictive simulation and for designing responsive, dexterous surgical manipulators. Furthermore, with increasing interest in 3D printing for rapid creation of soft robots consisting of flexible materials, the ability to easily acquire material properties from existing sensor data, such as medical images and videos, can help to replicate similar material properties. In this talk, I present recent advances to determine patient-specific tissue elastic parameters from images and videos, acceleration techniques, and application to medical applications. I conclude by discussing possible future directions and new challenges.
Madison (WI), USA
Dr. Jingshan Li received the BS, MS and PhD from Tsinghua University, Chinese Academy of Sciences, and University of Michigan, in 1989, 1992, and 2000, respectively. He was with GM R&D Center (20002006), and University of Kentucky (20062010). He is now a Professor in Department of Industrial and Systems Engineering, and Associate Director of Wisconsin Institute of Healthcare Systems Engineering, at University of Wisconsin-Madison. Dr. Li has published 1 textbook, 6 book volumes, 110 journal articles, 15 book chapters and 120 refereed conference proceedings. He is the Senior Editor of IEEE TASE and IEEE RAL, and Department, Area, and Associate Editor of many other journals. He was General and Program CoChair of 2013 and 2015 IEEE CASE, and is the Program Chair in 2019. He was the founding Chair of Technical Committee on Sustainable Production Automation and has been the Chair of TC on Automation in Healthcare Management since 2016. Dr. Li is an IEEE Fellow. He received the NSF CAREER Award, IEEE RAS Early Career Award, and multiple Best Paper Awards from IEEE TASE, IIE Transactions, IEEE CASE, and many flagship international conferences. His research interests are in design, analysis, improvement and control of production and healthcare systems.
Talk # 1
From Industry 4.0 to Healthcare 4.0: Problems, Opportunities, and Challenges in Smart and Interconnected Healthcare Systems
In recent years, there have been growing interests in healthcare systems research worldwide to improve care quality, patient safety, and operation efficiency. In this talk, we will first discuss the evolution of Industry 4.0, then introduce the idea of Healthcare 4.0, i.e., the smart and interconnected healthcare systems. We will present lessons we learned and results we obtained during the journey of from manufacturing systems research to healthcare delivery system study. We will introduce the problems and issues, and then address the difficulties and opportunities in healthcare delivery systems. In addition, we will provide a brief description of recent studies in healthcare delivery systems carried out at the Production and Service Systems Lab in University of Wisconsin-Madison. Finally, we will discuss the challenges and future directions in smart and interconnected healthcare systems research.
Talk # 2
Smart and Efficient Healthcare Delivery throughout the Journey of Patient Care
A patient’s care journey may cover many aspects of healthcare delivery, from primary care, specialty care, emergency, hospitalization, to nursing facility and home care. Ensuring safe, efficient, and seamless care across the spectrum is of significant importance. In this talk, we will introduce the modeling and analysis for studying and improving the connected spectrums of healthcare system a patient may encounter throughout the care delivery cycle. Specifically, the workflow and coordination in primary care, the diagnosis and test procedures, the inpatient care and transitions between emergency, ICU and hospital floor, and the discharge, readmission, and home care interventions, will be addressed. Stochastic models, such as Markov chain, queueing networks, as well as machine learning and optimization techniques, and system-theoretic approach, will be utilized to analyze and improve the system performance. Multiple case studies on the hospital and clinic floors will be introduced. The successful development of such studies will contribute to developing smart and efficient healthcare delivery throughout the patient’s journey.
Prof. Jie Song is an Associate Professor with the Department of Industrial Engineering and Management, Peking University.She received the B.S. degree in applied mathematics from Peking University, Beijing, China, in 2004, and the M.S. and Ph.D. degree in industrial engineering from Tsinghua University, Beijing, in 2007 and 2010, respectively. She has been a research fellow in Georgia Institute of Technology and University of Wisconsin, Madison. Her current research interest is to develop novel methods/tools from an industrial engineering’s perspective by sufficiently understand the dynamic nature of the complex service engineering system in an information-rich environment, and appropriately integrating online learning knowledge to make real-time decision with purpose to improve the efficiency and effectiveness of service engineering systems. Her research is supported by National Science Foundation of China, she has been honored the Chang Jiang Youth Scholar Award by Ministry of Education in China and many other faculty awards in Peking University. She is also the winner of Best Paper Award of 2014 IEEE CASE. She is an Associate Editor for IEEE Automation of Science and Engineering, Flexible Services and Manufacturing, Asia-Pacific of Operational Research. Prof. Song is a professional member of IEEE and INFORMS.
Talk # 1
Dynamic recommendation of physician assortment with patient preference learning
Web-based appointment systems are emerging in healthcare industry providing patients with convenient and personalized services, among which physician recommendation is becoming more and more popular tool to make assignments of physicians to patients. Motivated by a popular physician recommendation application on a web-based appointment system in China, this paper gives a pioneer work in modelling and solving the physician recommendation problem. The application delivers personalized recommendations of physician assortments to patients with heterogeneous illness conditions, and then patients would select one physician for appointment according to their preferences. Capturing patient preferences is essential for physician recommendation delivery, however, it is also challenging due to the lack of data on patient preferences. In this work, we formulate the physician recommendation problem, based on which the preference learning algorithm is proposed that optimizes the recommendations and learns patient preferences at the same time. Since the illness conditions of patients are heterogeneous, the algorithm aims to make personalized recommendation for each patient. Besides demonstrating the effectiveness of algorithm performance in terms of regret bound, we also provide extensive numerical experiments to show the expected algorithm performance under heterogeneous reward scenarios and performance comparison with algorithms in literature under fixed reward scenarios. We introduce the flexibility of adjusting preference estimate update interval into our algorithm, and conclude that short update interval contributes to short-term performance while long update interval leads to good results in the long run. Furthermore, we analyse how preference bound helps the algorithm to make explorations, which constitute two major contributions of our algorithm. Finally, we discuss the relevance between patient preferences and physician utilization, and present a utilization-balancing approach that is effective in numerical experiments
Maria Pia Fanti (IEEE Fellow) received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1983. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983, she has been with the Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy, where she is currently a Full Professor of system and control engineering and Chair of the Laboratory of Automation and Control. Her research interests include discrete-event systems; Petri net; consensus protocols; fault detection; management and modeling of complex systems, such as logistics, production and healthcare systems. She has published +270 papers and two textbooks on these topics. Prof. Fanti was General Chair of the 2011 IEEE Conference on Automation Science and Engineering and of the IFAC Workshop on Dependable Control of Discrete Systems 2009. She is Editor of the IEEE Trans. on Automation Science and Engineering and Associate Editor of the IEEE Trans. on Systems, Man, and Cybernetics: Systems. She is member at large of the Board of Governors of the IEEE SMC Society and of the IEEE Robotics and Automation Society, Co-Chair of the Technical Committee on Discrete Event Systems of the IEEE SMC Society, and of the TC on Automation in Logistics of the IEEE Robotics and Automation Society.
Talk # 1
New Approaches for Managing Logistics Systems: Integrating Information, Communication Technologies and Remote Sensing
Logistics systems of the future are expected to provide resource-efficient, sustainable, safe, equitable and timely handling of goods and management services for the benefit of economy and society, in order to sustain global supply chains and multimodal transportation systems. The increasing availability of artificial intelligence technologies, such as remote sensing, information and communication tools, big data, blockchain, Internet of Things and machine learning, can capture, elaborate and communicate historical and real-time data and provide opportunities for establishing cloud-based and collaborative logistic ecosystems. This talk will present how automation science has potential to enhance the performance of logistics systems by providing novel, integrated hardware and software solutions that affect the economics of different segments of the logistics chain and transportation, by improving throughput and reducing resource requirements and environmental impact. Moreover, the talk will consider novel management techniques and services based on the modern communications, remote sensing, automation and Internet of Things technologies, that are suitable for helping stakeholders and decision makers to manage and optimize logistics systems. Hence, the presentation will focus on the design of cloud-based platforms and Decision Support Systems enabling the integration of supply-chain-related transport processes through logistics artificial intelligence solutions. In this context, some results obtained in European projects frameworks will be discussed.
Talk # 2
Quantized Consensus Algorithm for Distributed Task Assignment: Results and Applications
The distributed coordination problem for networks of dynamic agents is an active research field, which attracts a significant interest due to the need to exploit the capabilities of large-scale networks and systems of the near future. This talk deals with a constrained distributed task assignment problem in which a set of different tasks has to be assigned to a group of agents by minimizing the maximum cost (typically the execution time) under communication, assignment and capacity constraints. To solve this problem, we provide a gossip-based discrete consensus algorithm that, starting from a random assignment of tasks, is able to reach a feasible solution while minimizing the global objective function by only local interactions among agents. The convergence properties and performances of the proposed gossip algorithm are characterized for two distinct network configurations, i.e., peer-to-peer net- works and proximity networks. A simulation based study validates the theoretical analysis by considering more general and complex scenarios. Finally, an application proposing a solution for the distributed dynamic assignment of a set of electric vehicles to a network of charging stations is presented.
Newark (NJ), United States
MengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology, China in 1983, M.S. degree in Automatic Control from Beijing Institute of Technology, China in 1986, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute, USA in 1990. He joined New Jersey Institute of Technology, USA in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 700 publications including 12 books, 400+ journal papers (300+ in IEEE transactions), 12 patents and 28 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man and Cybernetics Society. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of IEEE, IFAC, AAAS and Chinese Association of Automation (CAA).
Talk # 1
What is the Next Industrial Revolution?
Human beings have experienced two major industrial revolutions. The first one took place in the 19th century, which replaced muscle power from humans and animals with mechanical power. The second one started in the middle 20th century, which provided people and societies with Internet. It was built with the technologies from computing, communication, networking and information storage. Both offered unprecedented productivity increases. What will be the next one? This talk intends to answer this question by presenting some recent development of Internet of Things (IoT) and smart systems. IoT was selected by IEEE as a major initiative to develop and advance over the next few years. Several recent studies have predicted the huge growth of IoT and tremendous benefits to the world economy. It was expected that 26 billion IoT units would be installed by year 2020, generating $300 billion in revenue. The IoT will generate an additional $1.9 trillion in economic value. We plan to present a system engineering approach to Internet-of-Things-based smart systems and their applications to smart manufacturing, smart cities, smart gird, smart logistics, and smart healthcare services.
Talk # 2
Scheduling of Multi-robot Cluster Tools in Semiconductor
This talk intends to present Petri nets as a modeling, analysis, optimal scheduling and real-time control tool for single and multi-cluster tools that are widely used in semiconductor manufacturing industry. We illustrate how to use Petri nets to model various wafer production features involved in these highly expensive robotic manufacturing systems. Then we show how to use the resultant Petri net models to establish various schedulability conditions and derive extremely efficient algorithms that can compute optimal schedules for single and multi-cluster tools. When the bounded variation of activity time is caused in a fabrication process, we finally demonstrate how to adjust the scheduled robot wait time to offset such variation in order to achieve desired real-time optimal execution results. Our work focuses on those process-bounded cluster tools in which robots are fast enough such that they have some idle time in realizing an optimal schedule.
Beijing Institute of Technology, China
Toshio Fukuda received the B.S. degree in mechanical engineering from Waseda University, Tokyo, Japan, in 1971, and the M.S. and Ph.D. degrees in mechanical engineering from the University of Tokyo, Tokyo, in 1973 and 1977, respectively. From 1977 to 1982, he was in the National Mechanical Engineering Laboratory, Tsukuba, Japan. From 1982 to 1989, he was at the Science University of Tokyo, Tokyo. Starting in 1989, he was at Nagoya University, Nagoya, Japan, where he was a Professor in the Department of Micro System Engineering, and a Professor at Meijo University, Nagoya. He is currently a Professor (1000 Foreign experts Plan) with the Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China, where he is mainly engaged in the research fields of intelligent robotic systems, cellular robotic systems, mechatronics, and micro/nano robotics.
Toshio Fukuda was the President of IEEE Robotics and Automation Society (1998–1999), the Editor-in Chief of the IEEE/ASME TRANSACTIONS ON MECHATRONICS (2000–2002), the Director of Division X: Systems and Control (2001–2002), the IEEE Founding President of the Nanotechnology Council (2002–2003, 2005), the Director of Region 10 (2013–2014), and the Director of Division X: Systems and Control (2017–2018) and Foreign member of Chinese Academy of Sciences (2017).
Talk # 1
Mutli-Scale Robotic System --- From large scale cellular robot to small scale robots
This lecture is an overview of the Multi-scale robotics, based on the Cellular Robotics System, which is the basic concept of the emergence of intelligence in the multi-scale way from Cell Level to the Organizational Level, proposed more than 30 years ago. It consists of many elements how the system can be structured from the individual to the group/society levels in analogy with the biological system. It covers with the wide range of challenging topics: Then I mainly focus on medical robots and bio cell manipulation and cell assembly and refer to applied areas for the future hybrid cyborg and bionic system to improve the quality of life of human.
Arianna Menciassi is Full Professor of Biomedical Robotics at Scuola Superiore Sant’Anna (SSSA) and team leader of the “Surgical Robotics & Allied Technologies” Area at The BioRobotics Institute. She obtained the Master Degree in Physics (summa cum laude, 1995) at the Pisa University and the PhD in Bioengineering at SSSA (1999). She was Visiting Professor at the Ecole Nationale Superieure de Mecaniques et des Microtechniques of Besancon (France), and at the ISIR Institute at the Université Pierre et Marie Curie, in Paris. She has a substantial devotion to training and education, both at SSSA and at the University of Pisa, having served as preceptor to 15 postdoctoral associates, 20 PhD students and ~ 50 graduate degree recipients.
Her main research interests involve surgical robotics, biomedical robotics, microsystem technology and micromechatronics, with a special attention to the synergy between robot-assisted therapy and micro-nano-biotechnology-related solutions. She also focuses on magnetically-driven microrobots and microdevices, as well as on biomedical integrated platforms for magnetic navigation and ultrasound-based treatments. She carries on an important activity of scientific management of several projects European and extra-European, thus implying many collaborations abroad and an intense research activity. She is co-author of more than 370 scientific publications and 7 book chapters on biomedical robots/devices and microtechnology. She is co-Editor of a book on piezoelectric nanomaterials for biomedical applications. She is also inventor of 25 patents, national and international. She served until August 2013 in the Editorial Board of the IEEE-ASME Trans. on Mechatronics and she is now Topic Editor in Medical Robotics of the International Journal of Advanced Robotic Systems; she is Co-Chair of the IEEE Technical Committee on Surgical Robotics, she is in the Steering Committee of the Society for Medical Innovation and Technology and in the Steering Committee of the IEEE Transactions on Nanobioscience. In the year 2007, she received the Well-tech Award (Milan, Italy) for her researches on endoscopic capsules, and she was awarded by the Tuscany Region with the Gonfalone D’Argento, as one of the best 10 young talents of the region.
Talk # 1
Robotic technologies and micro-technologies for targeted therapy: challenge and opportunities
Robotic manipulators have been introduced for the first time in surgery in 1985, when a Puma 560 was used by Kwoh for performing neurosurgical biopsies with high precision. After that milestone, robots and robotic technologies have gained an increasingly important role in surgery, thanks to the accuracy and repeatability they could add to surgical tasks. From the original task of increasing accuracy and repeatability, robots today are asked to do more: they should be un-intrusive and flexible in terms of sharing control with human operators, they should perform better some tasks and they should reach areas normally not reachable by traditional surgical solutions. This talk introduces the key aspects of targeted therapy starting from the speaker experience on robotics for minimally invasive and computer assisted surgery. The quest for miniaturization and natural access to the targeted pathologies led to the development of diagnostic and surgical tools to be delivered with an endoluminal and transluminal approach - such as endoscopic capsules - and to be controlled and propelled by remote operation schemes from outside. In addition to the traditional control of remote devices into the body, external sources, such as magnetic fields, ultrasound waves or laser beams, have been used for stimulating internal devices and triggering some therapeutic effects from outside, in a non-invasive way. The quest for targeted therapy has recently opened new opportunities for robotic technologies, which are used more and more as controllers for the delivery of drugs embedded in nanobiotech vectors and as solutions for making therapy really localized in the area of interest, enabling on-demand release kinetics and eliminating (or strongly limiting) side effects. This talk aims to present the above mentioned trends, with the support of specific examples coming from the speaker experience and her collaboration network.
Prof. George G.Q. Huang is Chair Professor and Head of Department in Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. He gained his BEng and PhD in Mechanical Engineering from Southeast University (China) and Cardiff University (UK) respectively. He has conducted research projects in the field of Physical Internet (Internet of Things) for Manufacturing and Logistics with substantial government and industrial grants. He has published extensively including over two hundred refereed journal papers in addition to over 200 conference papers and ten monographs, edited reference books and conference proceedings. His research works have been widely cited in the relevant field. He serves as associate editors and editorial members for several international journals. He is a Chartered Engineer (CEng), a fellow of ASME, HKIE, IET and CILT, and member of IIE.
Talk # 1
Breaking Manufacturing Complexity and Uncertainty through Smart CPS Visibility and Traceability
The power of information sharing in managing “bull-whip” effects in a supply chain and between work units has been known well, but a cost-effective enabling mechanism is yet to be developed. This talk proposes a trilogy of creating, establishing and utilizing information visibility and traceability to substantially reduce complexity and uncertainty to a level that the breakthrough towards next-generation manufacturing is achieved.
Talk # 2
Through-Bound Manufacturing - "Zero Warehousing" Smart Manufacturing
Cyber-Physical Systems provide visbility and traceability from real-time data analytics. This talk discusses a new “through-bound” factory using visbility and traceability to hedge the time risk in inventory management instead of using expensive warehousing space.
The University of Electro-Communications, Chofu, Japan
Professor Tatsuo Arai received B.E. M.S. and PhD degrees from the University of Tokyo in 1975, 1977, and 1986, respectively. He joined the Mechanical Engineering Laboratory, the Japanese Government in 1977, and was engaged in research and development of new arm design and control, mobile robot, teleoperation, and micro robotics. He stayed at MIT as a visiting scholar in 1986-1987. He moved to Osaka University in 1997 as a full professor at the Graduate School of Engineering Science. In April 2017, he moved to Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, as a State 1000 Talent Program Professor. His current research topics are mechanism design including parallel mechanisms, legged working robots, micro robotics for bio application, human robot interaction. He has published more than 550 journals and reviewed conference papers on robotics, 5 books, and has 37 patents. He is IEEE Fellow, RSJ Fellow, JSME Fellow and IAARC director. He is a deputy editor-in-chief of the Robomech Journal. He worked for the Cabinet Office as a chair of the Technical Advisory Committee of the Destruction of Abandoned Chemical Weapon in 2000-2007. He was a project leader of National Project on Hyper Bio Assembler in 2011-2016.
Talk # 1
Service Robot and Its Safety
The service robot is a promising tool to provide daily care, support and welfare in our near future society. The service robot is defined in the World Robotics 2015 as such a way, “a robot that performs useful tasks for humans or equipment excluding industrial automation application.” As service robots would inevitably work near around people, they could make interaction with humans in not only physical but also psychological way. In the former sense the robot safety is crucial issue, however, in the latter also we need to take attention on another safety issue, that is, psychological or mind safety. What is the psychological safety? When you stay with some service robot which provides you daily services, i.e., bringing a cup of coffee, suggesting you some information, or whatever daily issues, you want to feel comfortableness, friendship, controllability, satisfaction of its good performance and no stress toward the robot. This sort of feeling is called “ANSHIN” in Japanese. The ANSHIN is another aspect of robot safety. The talk will cover the state-of-the-art of service robot technology and its ANSHIN issues.
Anibal T. De Almeida (PhD) is a Full Professor in the University of Coimbra and Director of the Institute for Systems and Robotics (UC). He has been responsible for over 40 funded national and international projects in the areas of industrial automation, robotics, advanced motors and drives. He has been Associate Editor of the journal IEEE Trans. on Industrial Electronics. He is one the Editors of the Energy Efficiency Journal from Springer. He was General Chair of major IEEE conferences: IEEE EPQU 2011 and IEEE IROS 2012. He is co-author of six books and over 300 papers in international journals and conferences. He was Member of the High Technology Panel of NATO Scientific and Environmental Affairs Division 1993-2000. He is a consultant of international institutions including the European Commission, US-Department of Energy, US-Agency for International Development, California Institute for Energy Efficiency, International Academy of the Environment, Electric Power Research Institute, Lawrence Berkeley Laboratory, UNDP, UNIDO GEF and CLASP. He is member of the Board of Directors of CLASP (Washington, USA).
Talk # 1
Energy Harvesting for Mobile Robots
Energy harvesting is a prominent research area which continues to grow at rapid pace, with potential application in a wide range of applications. In particular, one area with a large application potential is mobile robotics, in which energy storage capacity greatly limits their autonomy. By using energy harvesting is possible to run a robot during a significant long period of time, or even indefinitely. It should be noted that due to energy harvesting constrains, in terms of the amount of power that can be extracted from the environment, the mobile robots that can greatly benefit from this technology are in general small robots, with low energy consumption and low processing capabilities, such as robots used in swarm applications and environmental monitoring. Different technology options for energy harvesting and energy storage are presented.
Talk # 2
Eco-Design of Service Robots
Recent worldwide trends of the world show that it is very important to develop new systems for saving energy and creating alternative/new energy sources for many sectors of the technical systems, in particular, in the field of robotics and automation. Service robots that are being produced in bulk deserve special attention. Domestic robots include vacuum cleaning and lawn-mowing robots, but a variety of other product ideas are being developed, including food and beverage waiters, robots for handicapped or elderly assistance, and even automated butlers. The presentation will addresses strategies to optimize energy performance of service robots. Advanced energy efficient motors and drives, power management and energy storage are key tools for that purpose. The ecodesign approach is presented, leading to a reduction in energy consumption along with the environmental impact over their life cycle, as well as other benefits such as longer autonomy.
Keiko Homma received B.Sc and Ph.D degrees in Engineering from the University of Tokyo in 1989 and in 2004, respectively. In 1989 she joined the Mechanical Engineering Laboratory, which was reorganized into the National Institute of Advanced Industrial Science and Technology (AIST) in 2001. She is currently a senior researcher at Service Robotics Research Team, Robot Innovation Research Center, AIST. From 1995 to 1996 she was a visiting researcher at Helsinki University of Technology (current Aalto University). Dr. Homma is a member of IEEE, and her current research interest centers on assistive and therapeutic robot systems, including their safety aspects.
Talk # 1
Safety issues of assistive robots
Assistive robots have the following safety issues. - Many of the potential users of the robots, including elderly and handicapped people, are not trained to operate the robots. - There are people who do not operate the assistive robots by themselves but accept the effects and risks from the robots. - Safety of the assistive robots cannot be established by isolating the robots from the users. - An emergency stop may not ensure safety. For example, when a robotic walking assistant device suddenly stops by using the emergency stop, the user may fall down. Therefore, assistive robots must be designed with safety in mind. I will introduce studies related to safety of the assistive robots including development of a risk assessment assistance tool for the manufacturers of the assistive robots, development of test dummies for durability test of exoskeleton-typed physical assistant robots.
Karon MacLean is Professor in Computer Science at UBC, with degrees in Biology and Mechanical Engineering (BSc, Stanford; M.Sc. / Ph.D, MIT) and and time spent as a professional robotics engineer (Center for Engineering Design, University of Utah) and haptics / interaction researcher (Interval Research, Palo Alto). At UBC since 2000, MacLean's research specializes in haptic (touch) interaction: cognitive, sensory and affective design for people interacting with the computation we touch, emote and move with and learn from, from robots to touchscreens and the situated environment. MacLean leads UBC’s Designing for People interdisciplinary research cluster and CREATE graduate training program (20 researchers spanning 8 departments and 4 faculties dfp.ubc.ca), and is Special Advisor, Innovation and Knowledge Mobilization to UBC’s Faculty of Science.
Talk # 1
Making Haptics and its Design Accessible
Today’s advances in tactile sensing and wearable, Internet-of-things and context-aware computing are spurring new ideas about how to configure touch-centered interactions in terms of roles and utility, which in turn expose new technical and social design questions. But while haptic actuation, sensing and control are improving, incorporating them into a real-world design process is challenging and poses a major obstacle to adoption into everyday technology. Some classes of haptic devices, e.g., grounded force feedback, remain expensive and limited in range. I’ll describe some recent highlights of an ongoing effort to understand how to support haptic designers and end-users. These include a wealth of online experimental design tools, and do-it-yourself open sourced hardware and accessible means of creating, for example, expressive physical robot motions and evolve physically sensed expressive tactile languages. Elsewhere, we are establishing the value of haptic force feedback in embodied learning environments, to help kids understand physics and math concepts. This has inspired the invention of a low- cost, handheld and large motion force feedback device that can be used in online environments or collaborative scenarios, and could be suitable for K-12 school contexts; this is ongoing research with innovative education and technological elements. All our work is available online, where possible as web tools, and we plan to push our research into a broader open haptics effort.
Talk # 2
Making and Experimenting with Furry Robots with Feelings
Touch has a major role to play in human-robot interaction. Here, advances in tactile sensing, wearable and context-aware computing as well as robotics more broadly are spurring new ideas about how to configure the human-robot relationship in terms of roles and utility, which in turn expose new technical and social design questions. This talk will focus on my group’s recent work on haptic or physical human-robot interaction, where we aim to bring effective haptic interaction into people's lives by examining how touch (in either direction) can help address human needs with the benefit of both low-and high-tech innovation. I will give a sense of these efforts from three perspectives, each involving significant technical and evaluative design challenges: sensing emotive touch, designing expressive robot bodies and behaviours, and creating evaluative scenarios where participants experience genuine - and changing -emotions as they interact with our robots.
Angelika Peer is currently Full Professor at the Free University of Bozen-Bolzano, Italy. From 2014 to 2017 she was Full Professor at the Bristol Robotics Laboratory, University of the West of England, Bristol, UK. Before she was senior researcher and lecturer at the Institute of Automatic Control Engineering and TUM-IAS Junior Fellow of the Institute of Advanced Studies of the Technical University of Munich, Germany. She received the Diploma Engineering degree in Electrical Engineering and Information Technology in 2004 and the Doctor of Engineering degree in 2008 from the same university. Her research interests include robotics, haptics, teleoperation, human–human and human–robot interaction as well as human motor control.
Talk # 1
Humans in the Loop: From bilateral to autonomous control
In the past, working spaces of humans and robots were strictly separated, but recent developments have sought to bring them into close interaction. Starting from bilateral teleoperation, one of the earliest examples of human-in-the-loop systems, moving on to shared and supervisory control schemes and ending with examples of autonomous robots interacting and collaborating with humans, the talk will emphasize typical challenges faced in modelling and controlling systems that stay in close interaction with humans. Research questions like robust stability despite of human uncertain behaviour, recognition of human intention, plan and action from multimodal signals, design of shared control policies and adaptation schemes as well as challenges in evaluating performance of human-in-the-loop systems will be discussed.
Talk # 2
Introduction to Haptics, the Sense of Touch
The sense of touch is next to taste, sight, smell, and hearing one of our 5 human senses. Starting from a brief introduction to the physiology of touch, the talk will highlight a series of technological applications that benefit from the introduction of haptics. The current state of the art in the field will be discussed along with recent findings and developments allowing a listener to obtain a broad overview of the field of haptics.
Jee-Hwan Ryu received the B.S. degree in mechanical engineering from Inha University, South Korea, in 1995, and the M.S. and Ph.D. degrees in mechanical engineering from KAIST, South Korea, in 1995 and 2002, respectively. From 2002 to 2003, he worked as a post-doc researcher in the department of electrical engineering at the University of Washington, and at the similar time, he was also affiliated with the institute of robotics and mechatronics in DLR as a visiting scientist. Prior to joining KOREATECH, from 2003 to 2005, he was a research professor in the department of electrical engineering at KAIST. He is currently a full professor with the department of mechanical engineering, KOREATECH, South Korea. His research interests include haptics, telerobotics, exoskeletons, and autonomous vehicles. He has received several awards including IEEE Most Active Technical Committee Award as a Co-chair of TC Haptics in 2015, Best poster award in 2010 IEEE Haptic Symposium. He has been served as an Associate Editor in IEEE Transactions on Haptics, and since 2017, he has been serving as an Associate Editor-in-chief in World Haptics Conference. He was involved in many international conference organization, and especially, he is working as a general chair of AsiaHaptics2018.
Talk # 1
Twisted String Actuator and its Application to Wearable Soft Exosuit
Even with the recent enormous advancement of software and hardware technology in robotics, it is quite frustrating that most of the exoskeletons are still quite heavy and too rigid to be wearable. One of the major bottleneck is the limited power-to- weight ratio and the rack of softness of actuators. In particular, rigid and heavy mechanical transmission system has been dragging down the advancement of the wearable exoskeleton technology. In this presentation, I’m going to introduce some of the recent development of Twisted String Actuator (TSA) as an effort to increase the power-to-weight ratio and softness of the actuator. Typically, I want to focus on basic mathematical model, several extended modules and implementations of this. I will also touch several mechanisms to overcome the limitation of the TSA such as nonlinearity and low contraction speed. In additional to the basics of TSA, as an example of the implementation, I will be showing a soft upper limb exosuit together with several different version of soft hand exoskeleton systems.
Talk # 2
How Stiff or Light we can Reach: Time-domain Passivity Approach for Stable and Transparent Haptic Interaction
The addition of haptic capability dramatically increases the immersiveness of human- robot interaction in AR/VR or teleoperation. That is because the sense of touch conveys rich and detailed information about virtual or remote environments. However, it has been challenging to provide immersive feelings of touch due to the limited range of impedance that a haptic device can display without any stability issue. In this presentation, we will be discussing how to realize stable and immersive human-robot haptic interaction, in particular from the aspect of tight haptic coupling between human and virtual/remote environment. Several state-of-the-art control methods, such as Time Domain Passivity Approach, Successive Stiffness Increment, Successive Force Augmentation method will be introduced, which have been developed for increasing the impedance range of both impedance type and admittance type haptic interfaces for the interaction with virtual objects and remote environments. A stable bilateral teleoperation method to overcome time varying communication delay will be introduced as well with several implementation examples with DLR space telerobotic systems.
Tamim Asfour is full Professor of Humanoid Robotics at the Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies at the Karlsruhe Institute of Technology (KIT). His research focuses on the engineering of high performance 24/7 humanoid robotics as well as on the mechano-informatics of humanoids as the synergetic integration of mechatronics, informatics and artificial intelligence methods into humanoid robot systems, which are able to predict, act and interact in the real world. Tamim is the developer of the ARMAR humanoid robot family. In his research, he is reaching out and connecting to neighboring areas in large-scale national and European interdisciplinary projects in the area of robotics in combination with machine learning and computer vision. Tamim is the Founding Editor-in-Chief of the IEEE-RAS Humanoids Conference Editorial Board, co-chair of the IEEE-RAS Technical Committee on Humanoid Robots (2010-2014), Editor of the Robotics and Automation Letters, Associate Editor of Transactions on Robotics (2010-2014). He is scientific spokesperson of the KIT Center “Information ? Systems ? Technologies (KCIST)”, president of the Executive Board of the German Robotics Society (DGR) and member of the founding Board of Directors of euRobotics (2013-2015).
Talk # 1
Engineering Humanoid Robots to Assist Humans and Augment Human Performance
Humanoid robotics has made significant progress and will continue to play central role in robotics research and many applications of the 21st century. Engineering complete humanoid robots, which are able to learn from human observation and sensorimotor experience, to predict the consequences of actions and exploit the interaction with the world to extend their cognitive horizon remains a research grand challenge. The talk will address recent progress towards building integrated 24/7 humanoid robots able to perform complex grasping and manipulation tasks and to learn from human observation and sensorimotor experience. In particular, I will show how learning from human observation and natural language methods can be combined to build a motion alphabet and robot internet of skills as the basis for intuitive and flexible robot programming. In the second part of the talk, I will discuss the vision of robot suits for every human body and the transformative impact of humanoid robotic on other research areas and application fields. As a result, humanoid robots will become 24/7 wearable robot companions for augmentation or replacing human performance in daily and working environments, and humanoid technologies will contribute to personalized rehabilitation in medicine, human support and protection in human-made and natural disasters.
Talk # 2
Generation of multi-contact whole-body motions based on natural language models learned from human motion data
Generating multi-contact whole-body motions for humanoid robots and whole-body exoskeletons constitutes an open and, due to its high dimensionality, a very challenging problem of vital interest for humanoid robotics research. In this talk, we present a data-driven approach for generating sequences of whole-body poses with multi-contacts, which is inspired by techniques from natural language processing. The approach uses human motion data for the autonomous generation of sequences of whole-body pose transitions for whole-body tasks that use the environment to enhance balance. To this end, we present a large-scale whole body human motion database together with techniques for systematic organization, annotating, classification of human motion data as well as for contact-based segmentation of whole-body motion into support poses. These poses are subsequently used to train an n-gram language model, whose words are whole-body poses and whose sentences are sequences of these poses that characterize a motion. Using this language model, a sequence of whole-body pose transitions, which satisfies the constraints imposed by the task is generated as the sequence of transitions with the highest probability according to the language model.
Stanford (CA), United States
Oussama Khatib received his PhD from Sup’Aero, Toulouse, France, in 1980. He is Professor of Computer Science and Director of the Robotics Laboratory at Stanford University. His research focuses on methodologies and technologies in human-centered robotics. He is a Fellow of IEEE, Co-Editor of the Springer Tracts in Advanced Robotics (STAR) series, and the Springer Handbook of Robotics. Professor Khatib is the President of the International Foundation of Robotics Research (IFRR). He is recipient of the IEEE RAS Pioneer Award, the George Saridis Leadership Award, the Distinguished Service Award, the Japan Robot Association (JARA) Award, the Rudolf Kalman Award, and the IEEE Technical Field Award. In 2018, Professor Khatib was elected to the National Academy of Engineering.
Talk # 1
The Age of Human-Robot Collaboration
Robotics is undergoing a major transformation in scope and dimension with accelerating impact on the economy, production, and culture of our global society. The generations of robots now being developed will increasingly touch people and their lives. They will explore, work, and interact with humans in their homes, workplaces, in new production systems, and in challenging field domains. The emerging robots will provide increased support in mining, underwater, hostile environments, as well as in domestic, health, industry, and service applications. Combining the experience and cognitive abilities of the human with the strength, dependability, reach, and endurance of robots will fuel a wide range of new robotic applications. The discussion focuses on design concepts, control architectures, task primitives and strategies that bring human modeling and skill understanding to the development of this new generation of collaborative robots.
Tomomichi Sugihara is an associate professor at Department of Adaptive Machine Systems, Graduate School of Engineering, Osaka University. He received his BS and MS degrees in mechanical engineering from the University of Tokyo, Japan, in 1999 and 2001, respectively. He also received his PhD from the University of Tokyo in 2004. He was an academic research assistant from 2004 to 2005 at the University of Tokyo, and became a research associate. He worked at Kyushu University as a guest associate professor from 2007 to 2010. He moved to Osaka University in 2010 and held the current position. He is the principal investigator of Motor Intelligence Laboratory. His research interests include kinematics and dynamics computation, motion planning, control, hardware design, and software development of anthropomorphic robots. He also studies human motor control based on robotic technologies. He is a member of IEEE.
Talk # 1
Dynamics Morphing --- a paradigm to integrate various robot motions
"Dynamics morphing" is a paradigm to enable a robot to do not only individual motions but also seamless transitions between them. The bipedalism, for instance, implies a capability of locomoting in environments by discontinuously alternating the support leg and keeping standing at the desired point. From the viewpoint of dynamical system analysis, to stand means to asymptotically stabilize the system, while to step means to locally destabilize the system and move to another stable equilibrium. A self-excited cycle of the above stabilization and destabilization forms a walking. Such an interpretation of motions as dynamical systems suggests a controller design to integrate various motions into a `morphing' dynamical system, which provides the robot with flexibility against perturbations. In this talk, the above concept and some implementations of a humanoid robot controller that has been enhanced to a variety of behaviors are presented.
Talk # 2
Successive information processing for a biped locomotion in the real world
In this talk, how to design the locomotion system of a biped robot that can move flexibly and robustly in the real world is discussed. The robot should perceive how the world and the robot itself are and determine how to behave in real-time even from uncertain, incomplete, noisy and unpredictable sensory information. The overall system should not form a sequential architecture, where each process waits for complete information from the former process, as employed in the conventional systems, but "the subsumption architecture", where a homeostatic controller works in the lower layer and upper layer subsumes it. This is not easy since each process including navigation, mapping and perception has to be implemented in a successive way, i.e., with a form of differential equation rather than a batch process. Some key technologies to build up such a system are presented.
Sarthak Misra joined the University of Twente in 2009. He is currently a Full Professor in the Department of Biomechanical Engineering. He is also affiliated with the Department of Biomedical Engineering, University of Groningen and University Medical Center Groningen. Sarthak obtained his doctoral degree in the Department of Mechanical Engineering at the Johns Hopkins University, Baltimore, USA. Prior to commencing his studies at Johns Hopkins, he worked as a dynamics and controls analyst at MacDonald Dettwiler and Associates on the International Space Station Program. Sarthak received his Master of Engineering degree in Mechanical Engineering from McGill University, Montreal, Canada. He is the recipient of the European Research Council (ERC) Starting and Proof-of-Concept grants, Netherlands Organization for Scientific Research (NWO) VENI and VIDI awards, Link Foundation fellowship, McGill Major fellowship, and NASA Space Flight Awareness award. He is the co-chair of the IEEE Robotics and Automation Society Technical Committee on Surgical Robotics, and area co-chair of the IFAC Technical Committee on Biological and Medical Systems. Sarthak’s broad research interests are primarily in the area of applied mechanics at both macro and micro scales. He is interested in the modeling and control of electro-mechanical systems with applications to medical robotics.
Talk # 1
Wireless Control of Miniaturised Agents
Medical robotic systems strive to make surgical interventions less invasive, less risky for both patients and clinicians, more efficient, and capable of achieving better patient outcomes. Increasing the targeting accuracy during robot-assisted minimally invasive surgical procedures requires the integration of pre-operative plans and intra-operative control. In this talk, I will discuss how wirelessly controlled agents might offer advantages in terms of reduced invasiveness and untethered access to deep-seated regions within the human body. On that account, this talk covers the closed-loop control of microparticles, miniaturised hydrogel grippers, microjets, and magnetosperms.
Université Bourgogne Franche-Comté, Besancon, France
Micky Rakotondrabe has been an associate professor since 2007 at the Université Bourgogne Franche-Comté with research appointment at FEMTO-ST institute. His research fields deal with the design, modeling, signal estimation and control techniques for piezoelectric systems with applications on microrobotics and automation at small scale. He is the founder and head of the MACS (methodologies for the design and control of mechatronic systems) research group at FEMTO-ST and of the GREEM (control for green mechatronics) international master at the Université Bourgogne Franche-Comté. He obtained the Université de Franche-Comté 2006 best-phd-thesis finalist; the IEEE-ICARCV-2006 best-paper-award finalist, the IEEE-CASE-2011 best-application-paper-award finalist and the 2011 Romanian-Scientific-Activity-Award. He holds the French-Excellence-Activities-Award since October 2011. He participated to the control of the French-team mobile microrobot that holds several times the IEEE-NIST Mobile Microrobot International Challenge Awards (1st prize on speed in 2010, 2011 and 2012). The paper 'Complete open loop control of hysteretic, creeped and oscillating piezoelectric cantilever' was among the 5 most cited papers of the IEEE Transactions on Automation Science and Engineering of the 2013 year. In 2016, he received the Big-On-Small award which is to recognize a young professional with excellent performance and international visibility in the topics of manipulation, automation and robotics at small scales.
Talk # 1
Piezoelectric systems for small scale tasks: design, modeling, control and signal estimation
Piezoelectric materials have strong recognition in the development of actuators, systems and microrobots devoted to work at small scale. This recognition is thanks to their high resolution (down to nanometer), high bandwidth (in excess of the kiloHertz), high force density for certain piezomaterials, ease of integration (driven by electricity), and physical reversibility (usable for sensors or for actuators). In counterpart, piezoelectric systems exhibit low deformation (classically 0.1%), they suffer from nonlinearities and some of them exhibit badly damped vibration. These behaviors drastically damage or degrade the final tasks to be executed, such as precise positioning, imaging or manipulation. This talk describes first my works on the developments of piezoelectric systems that account for these limitations during the design. Then, I present their modeling for control purpose in order to reach certain severe performances when working at small scale. Because measurement is essential in control, I finally present some measurement and estimation techniques among which self-sensing approach is one of the feature for piezoelectric systems. In the meantime I will highlight how measurement is still a great challenge in automation at small scale due to the lack of convenient sensors.
Talk # 2
Control with sensors minimization in piezoelectric systems working at small scale
Piezoelectric materials have strong recognition in the development of actuators, systems and microrobots devoted to work at small scale. This recognition is thanks to their high resolution (down to nanometer), high bandwidth (in excess of the kiloHertz), high force density for certain piezomaterials, ease of integration (driven by electricity), and physical reversibility (usable for sensors or for actuators). In counterpart, piezoelectric systems exhibit low deformation (classically 0.1%), they suffer from nonlinearities and some of them exhibit badly damped vibration. These behaviors drastically damage or degrade the final tasks to be executed, such as precise positioning, imaging or manipulation. In this talk, the challenge in controlling piezoelectric systems working at small scale is discussed. Their control and automation suffer from the lack of convenient and embedded sensors to complete the real-time measurement and feedback. The first part of the talk deals with an alternative feedback control architecture based on the piezoelectric self-sensing approach. This approach permits a real-time and fully embedded measurement, or almost. In the second part, open-loop or feedforward control architecture for piezoelectric systems is presented. The advantage of this approach is its full integration and its low cost, though robustness could be compromised in some case.
Dr. Yajing Shen received the PhD degree in 2012 from Fukuda Lab., Nagoya University, Japan, and he is working as Assistant Professor in Mechanical and Biomedical Engineering Department in City University of Hong Kong currently. His mainly research interest is micro/nanorobotics, including the micro/nano robots development and their applications in the fundamental and practical problems in biomedical, material, and other emerging fields. He has published ~100 papers in international journal/conference, and received serval awards, including the Best Manipulation Paper Award in IEEE International Conference on Robotics and Automation (ICRA) in 2011, the IEEE Robotics and Automation Society Japan Chapter Young Award in 2011, the Early Career Awards of Hong Kong UGC in 2014, Big-on-Small Award at MARSS 2018. He is a Senior Member of IEEE and an Executive member of China Micro-nano Robotic Society, and is very active in promoting micro/nano robotics to society, such as by serving as the committee member of international conference, organizing “micro/nano robot” workshop, special issue, and so on.
Talk # 1
Micro-Nano Robotic Systems in Biomedical Applications
Robotics have played important roles in biomedical applications owing to its advantages in precision, stability, dexterity and productivity. With the rapid development of micro-nano technology, the micro-nano robotic systems have received increasing attentions in recent years, which can be classified to two different categories according to their working principles and functions: (1) the robots can operate object with micro-nano scale resolution. (2) the robot is of micro-nano scale size. This talk will give two specific examples to demonstrate the above two types of robotic system in biomedical applications, i.e., one is the robot-aided high precision nano characterization system, and the other is the bio-inspired miniature robots. Lastly, the prospects of the micro-nano robotic systems will be discussed.
Sven Behnke is professor for Autonomous Intelligent Systems at the University of Bonn and director of the Institute of Computer Science VI. He received his MS degree in Computer Science (Dipl.-Inform.) in 1997 from Martin-Luther-Universität Halle-Wittenberg. In 2002, he obtained a PhD in Computer Science (Dr. rer. nat.) from Freie Universität Berlin. He spent the year 2003 as postdoctoral researcher at the International Computer Science Institute, Berkeley, CA. From 2004 to 2008, Professor Behnke headed the Humanoid Robots Group at Albert-Ludwigs-Universität Freiburg. His research interests include cognitive robotics, computer vision, and machine learning.
Talk # 1
Perception and Planning for Mobile Manipulation in Complex Environments
Robots need to perceive their environment to act in a goal-directed way. While mapping the environment geometry is a necessary prerequisite for many mobile robot applications, understanding the semantics of the environment will enable novel applications, which require more advanced cognitive abilities. In the talk, I will report on methods that we developed for learning tasks like the categorization of surfaces, the detection, recognition, and pose estimation of objects, and the transfer of manipulation skills to novel objects. By combining dense geometric modelling – which is based on registration of measurements and graph optimization – and semantic categorization – which is based on deep learning and transfer learning – 3D semantic maps of the environment are built. We demonstrated the utility of semantic environment perception with cognitive robots in multiple challenging application domains, including domestic service, space exploration, search and rescue, and bin picking.
Talk # 2
Learning Semantic Perception for Cluttered Bin Picking
Picking objects from cluttered piles is a challenging task with great application potential, e.g. in warehouses or domestic service applications. A key prerequisite for cluttered bin picking is the understanding of complex manipulation scenes. In the talk, I will report on efficient methods that we developed for learning tasks like semantic segmentation, object detection, pose estimation of objects, and the transfer of manipulation skills to novel objects. Our team demonstrated the utility of semantic environment perception in multiple challenging bin picking demonstrations, including the Amazon Picking Challenge, the European Robotics Challenges 1 and 2, and the FP7 project STAMINA.
Boston (MA), USA
Alois C. Knoll is a computer scientist and professor at Technical University Munich TUM. He teaches and conducts research in the fields of autonomous and embedded systems, robotics and artificial intelligence. In 2011, he founded the interdisciplinary course "Robotics, Cognition, and Intelligence" at TUM, which has become one of the largest MSc programs in TUM’s CS Dept. In 2007 he became a member of the EU's highest advisory board for information technology, ISTAG. In this function, he was in involved in the development of the EU flagship projects and was ab author of the report "European Challenges and Flagships 2020". In 2009, he co-founded "fortiss", the Munich Institute for Software, which has since been transformed into a state institute of Bavaria. He has coordinated the project ECHORD++, an initiative to bring together the robotics industry and universities to speed up robot technology’s route to market. He has been head of the sub-project "Neurorobotics" of the EU flagship project “Human Brain Project”. Since 2011 he has been PI at TUMCREATE, a joint venture of NTU and TUM-Asia in Singapore. His focus there is on modeling, simulation and optimization for infrastructure, both in methodical development and in the construction of practical systems.
Talk # 1
Introduction to Neurorobotics Platform with EU Human Brain
Abstract of Talk Tentative Talk Content: In conjunction with future computing, HBP’s robotics research plays multiple, significant roles in the HBP: (Closed Loop Studies): it links the real world with the “virtual world of simulation” by connecting physical sensors (e.g., cameras) in the real world to a simulated brain. (Brain-Derived Products): it links brain research to information technology by using scientific results (e.g., data, and models of behaviour) obtained in brain research and refining it to a readiness level where it can be used by commercial companies and easily taken up and rapidly turned into new categories of products. (Virtualised Brain Research): it links information technology to brain research by designing new tools for brain researchers, with which they can design experiments and then carry them out in simulation. We envision that the unique integration of the above three paths will lead to widespread mutually beneficial fertilization and research acceleration through the two-way inspiration of the involved disciplines.
Zhijun Li received the Ph.D. degree in mechatronics, Shanghai Jiao Tong University, P. R. China, in 2002. From 2003 to 2005, he was a postdoctoral fellow in Department of Mechanical Engineering and Intelligent systems, The University of Electro-Communications, Tokyo, Japan. From 2005 to 2006, he was a research fellow in the Department of Electrical and Computer Engineering, National University of Singapore, and Nanyang Technological University, Singapore. Since 2017, he is a Professor in Department of Automation, University of Science and Technology of China,. From 2016, he has been the Co-Chairs of Technical Committee on Biomechatronics and Bio-robotics Systems (B2S), IEEE Systems, Man and Cybernetics Society, and Technical Committee on Neuro-Robotics Systems, IEEE Robotics and Automation Society. He is serving as an Editor-at-large of Journal of Intelligent & Robotic Systems, and Associate Editors of several IEEE Transactions. He has been the General Chair and Program Chair of 2016 and 2017 IEEE Conference on Advanced Robotics and Mechatronics, respectively. Dr. Li’s current research interests include service robotics, teleoperation systems, nonlinear control, neural network optimization, etc.
Talk # 1
Development of Key Technology for Wearable Robots and Its Applications
Recently, as the aged population is increasing, many kinds of wearable robot have attracted much attention. In this talk, we present kinds of wearable robots to achieve many activities of daily living
(ADL). The developed service robots include upper limb exoskeleton, lower limb exoskeleton, intelligent robotic wheelchairs, wheeled inverted transportation, and interactive tour guide operation robot, and neural prosthesis, bionic hands, etc. The various advanced control approaches including skill transfer technique, intelligent control, bio-feedback control have been proposed and verified in these developed platforms.
Talk # 2
Human Cooperative Exoskeleton
The development of robotic systems capable of sharing with humans the load of heavy tasks has been one of the primary objectives in robotics research. At present, in order to fulfill such an objective, a strong interest in the robotics community is collected by the so-called wearable robots, a class of robotics systems that are worn and directly controlled by the human operator. Wearable robots, together with powered orthoses that exploit robotic components and control strategies, can represent an immediate resource also for allowing humans to restore manipulation and/or walking functionalities. The talk deals with wearable robotics systems capable of providing different levels of functional and/or operational augmentation to the human beings for specific functions or tasks. Prostheses, powered orthoses, and exoskeletons are described for upper limb, lower limb, and whole body structures.
Bronxville (NY), USA
Dr. Chun-Yi Su is a Professor of Mechanical, Industrial and Aerospace Engineering at Concordia University, Montreal, QC, Canada, and holds the Concordia University Research Chair in Control. He received his Ph.D. degree in control engineering from the South China University of Technology in 1990. He joined Concordia University in 1998, after a seven-year stint with the University of Victoria, Victoria, BC, Canada. He also held some visiting positions including Chair Professor of Chang Jiang (Cheung Kong) Scholars Program in China and JSPS Invitation Fellow in Japan. His research covers control theory and its applications to robotic systems. Dr. Su has authored or coauthored over 250 journal publications in robotics, mechatronics and control that have resulted in over 10,500 citations. Dr. Su has delivered over 80 invited talks, including plenary conference presentations and seminars. He was a recipient of several best conference paper awards. He has been on the Editorial Board of a few journals, including IEEE Transactions on Automatic Control and IEEE Transactions on Control Systems Technology. He has also served for many conferences as an organizing committee member.
Talk # 1
Impedance Control of Powered Exoskeletons for Human-Cooperative Manipulations Using Biological Signals
Powered exoskeletons have attracted much attention over the past few years and can be applied from military usage to patient rehabilitation. The exoskeletons are developed to augment the human’s muscular force and endurance that can not only perform the cooperation with the humans but also assist or supplement the human motion. Therefore, it is essential that the developed exoskeletons could exhibit biological behavior and performance. Considering human joints, one of the important features is the physical properties of the musculotendinous and their resultant impedance. However, the impedance profiles of the human joints vary substantially during motion. Therefore, exoskeletons should accordingly respond and adapt to these impedance profiles. This talk presents methods to develop adaptive impedance control of exoskeletons using biological signals. First, a reference musculoskeletal model of the human upper limb is developed and experimentally calibrate the model to match the operator’s motion behavior. Then, an impedance algorithm is proposed transferring stiffness from human operator through the surface electromyography (sEMG) signals, being utilized to design the optimal reference impedance model. In order to verify the proposed approach, the actual implementation has been performed using a real robotic exoskeleton and a human operator.
Talk # 2
Development of Mind Control System for Robotic Manipulators Fused with Visual Technology
With the advances in robot control (RC) systems, the relationship between humans and robots has thus become increasingly intimate, and many human-robot collaboration systems have been developed. However, it is hard for a disabled person to operate a robot because of the loss of motion capacity or reduced sensing ability. This talk will summarize the development of an intelligent shared control system for a robotic manipulator that is commanded by the user’s mind. The target objects are detected by a vision system and then displayed to the user in a video that shows them fused with flicking diamonds. Through the analysis of the invoked electroencephalograph EEG signals, a brain computer interface (BCI) is developed to infer the exact object that is required by the user. These results are then transferred to the shared control system, which is enabled by visual servoing (VS) techniques to achieve accurate object manipulation. Extensive experimental studies are performed to verify the performance of the developed mind control system.
Fabio Bonsignorio is a Visiting Professor at the Biorobotics Institute of the Scuola Superiore Sant’Anna in Pisa. He has been professor at the University Carlos III of Madrid until 2014 ( in 2009 he was awarded there the Santander Chair of Excellence in Robotics). He is founder and CEO of Heron Robots, see www.heronrobots.com. He has been working in the R&D departments of several major companies for more than 20 years. He is currently a member of the Research Board of Directors of SPARC. He has pioneered and introduced the topic of Reproducible Research and Benchmarking in Robotics and AI. He coordinated the EURON Special Interest Group on Good Experimental Methodology and Benchmarking in Robotics, is cochair of the IEEE RAS TC-Pebras. He has been general co-chair of the IEEE RAS 2015 Summer School on Replicable and Measurable Robotics Research. He has been the corresponding editor of the Special Issue on Replicable and Measurable Robotics Research on IEEE Robotics and Automation Magazine, appeared in September 2015. He has designed the Reproducible Articles in IEEE R&A Mag. He has been in the Program Commitee of the European Robotics Forum 2017, 2018 and he is for 2019.
Talk # 1
Reproducible Research in Robotics
The second wave of Robotics, integrating Machine Learning, Probabilistic Robotics and some AI is already having significant impact on our economy and our society. The third wave inspired by the organizational principles of living being and natural intelligence and merging more and more tightly with humans will potentially have a disruptive impact on society and our self-perception and very nature. Meanwhile methodology is lacking, societal and economical impact not well understood, citizen involvement in the issues still too limited. We need first of all to go back to the basics of the scientific method.
Talk # 2
Robotics is coming of age: Reproducible Research, Benchmarking, Insurance fees and the Economy of Robots
The second wave of Robotics, integrating Machine Learning, Probabilistic Robotics, and some AI is already having significant impact on our economy and our society. The third wave inspired by the organizational principles of living beings and natural intelligence and merging more and more tightly with humans will potentially have a disruptive impact on society and our self-perception and very nature. Meanwhile methodology is lacking, societal and economical impact is not well understood, and citizen involvement in the issues still too limited. Do we need first of all to go back to the basics of the scientific method? This seminar will cover issues about reproducible Robotics research, claim assessment, qualitative result evaluation, benchmarking of the performance of robotic and intelligent systems, and risk modelling.
Gaithersburg (MD), USA
Elena Messina leads the Manipulation & Mobility Systems Group of the Intelligent Systems Division (ISD) at the National Institute of Standards and Technology (NIST). Her current responsibilities include managing the Engineering Laboratory's Robotic Systems for Smart Manufacturing Program, which is focused on advancing the capabilities of agile, collaborative robots through the definition of performance requirements, metrics, test methods, tools, and testbeds. She is internationally recognized for her work in the development of performance metrics and evaluation methodologies for robotic and autonomous systems. Elena founded key efforts to develop test methodologies for measuring performance of robots, which range from long-term use of robotic competitions to drive innovation to consensus standards for evaluating robotic components and systems. Elena has over 150 publications and is co-editor of the books “Intelligent Vehicle Systems: A 4D/RCS Approach," "Performance Evaluation and Benchmarking of Intelligent Systems," and “Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor.” She has received two Department of Commerce Bronze Medals for Superior Performance and Technical Leadership and the Edward Bennet Rosa Award for research and development leading to standardized test methods for emergency response robots.
Talk # 1
Advancing the State of the Art in Robotics through a Holistic Approach to Competitions
Competitions are a useful tool for measuring robotic system and component performance. Are there certain practices and approaches that may have greater impact? I will discuss some lessons-learned from competitions that may prove useful in selection of metrics and design of tests and benchmarks. In certain cases, conceiving of a competition within a greater ecosystem of innovation can yield greater advancements.
Talk # 2
What Do We Talk About When We Talk About Robot Performance?
Robot systems and their constituent components, such as sensors and hands, are advancing at what feels like an accelerating pace. This progress is great news, but poses many challenges in being able to understand which robot, algorithm, or components would be appropriate for one's applications. Many claims are made in research papers and product brochures that are hard to translate to a particular real-world prediction of how well a robot would perform. We will discuss approaches for identifying key performance parameters in order to characterize a robot's performance. One of the key considerations is the fact that performance cannot be discussed in isolation: performance is always contextual.
Angel P. del Pobil received the B.Sc. in physics and the Ph.D. in engineering from the University of Navarra, Spain. He is currently a Professor with Jaume I University, Spain, where he is the founding Director of the Robotic Intelligence Laboratory. He is a Visiting Professor at Sungkyunkwan University, Korea. He is co-Chair of the IEEE RAS TC on Performance Evaluation & Benchmarking of Robotic Systems (2009-present), and a member of the Governing Board of the Intelligent Autonomous Systems Society (2012-2024). He was Co-Chair of the IEEE RAS Robot Motion & Path Planning TC (1997-2004) and member of the Governing Board of EURON (European Robotics Research Network, 2001-2009). He has over 300 publications, including 11 books, and was co-organizer of some 50 workshops and tutorials at ICRA, IROS, RSS, etc. He has been Program or General Chair of 6 international conferences such as Adaptive Behaviour (SAB 2014). He serves regularly as Associate Editor for ICRA and IROS, and on the program committee of over 180 international conferences. He has been invited speaker of 73 plenary speeches, seminars and tutorials, in 15 countries. He serves as associate or guest editor for 12 journals and has been PI of 37 research projects.
Talk # 1
Manual Robotic Intelligence -and the Success of Assistant Robots
Many forecasts predict a dramatic increase in the non-industrial robotics market in the coming years. For actual assistant robots to become consumer products, a leap in their manipulation skills is called for. If they are intended to help in performing daily tasks at home, the perceived quality of service requires a successful physical interaction with the environment. This poses a number of challenges such as adaptability, autonomy, functionality, resiliency, cost-effectiveness, and safety. I will also look at robots as cyber-physical networked systems and consider the posibilities of cloud computing. I will compare them with robots in online shopping warehouses, with some lessons learned from our participation in the Amazon Robotics Challenge 2017.
Talk # 2
Robots as Cyberphysical Systems: the Case of Personal Assistants and Online Shopping Warehouses
An intelligent robot is a perfect paradigm of a cyber-physical system (CPS), since its very nature is based on the seamless integration of computational algorithms and physical components, including embedded sensors, processors and actuators in order to sense and interact with the physical world. In my speech I will address some of the challenges for robots considered as CPS, such as adaptability, autonomy, functionality, resiliency, and safety, with emphasis on the physical interaction with the environment. As test cases I will consider robots as personal assistants, along with robots in online shopping warehouses, as an example towards the 4th industrial revolution, the so-called Industry 4.0, with some lessons learned from our recent participation in the Amazon Robotics Challenge 2017 that took place in Nagoya in July 2017. I will also discuss some implications in terms of the interactions of information processing, communication and control of physical processes, with especial emphasis on the difficulties that dealing with open-ended physical entities can bring.
Anca Dragan is an Assistant Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, where the focus is on algorithms for human-robot interaction -- algorithms that move beyond the robot's function in isolation, and generate robot behavior that also accounts for interaction and coordination with end-users. The lab works across different applications, from assistive robots, to manufacturing, to autonomous cars, and draw from optimal control, planning, estimation, learning, and cognitive science. She also helped found and serve on the steering committee for the Berkeley AI Research (BAIR) Lab, and is a co-PI of the Center for Human-Compatible AI. She has been honored by the Sloan Fellowship, MIT TR35, the Okawa award, and an NSF CAREER award.
Talk # 1
Optimal robot action for and around people
The traditional robotics problem is one of optimization. An engineer writes down a cost function and potentially a set of constraints, thereby specifying what it means for a robot to accomplish its task. The robot then is in charge of finding the behavior that is optimal for this specification. Thus, the focus in robotics is on how a robot can produce optimal (or even feasible) behavior despite the intricacies of operating in the real world. What drives my research is the realization that we are not building robots to work in some isolated universe, optimizing some exogenously specified cost function. We are building them to work in our universe, in order to help us. First, robots will not act in isolation. They will work with and around us. This makes optimal action in isolation far from sufficient – robots will need to choose actions that mesh well with ours. If there were no people on the roads, autonomous driving would be nearly solved. Instead, cars need to coordinate with us. So do quadrotors flying in our spaces, or assistive arms in our homes. My work formalizes the problem of optimal coordination with people, and introduces real-time solutions for continuous and high-dimensional state and action spaces. Second, robots will need to do what we want them to. This makes the notion of some exogenously specified cost function a myth. Cost functions don’t just fall from the sky and incentivize the robot behavior we want. Thus, figuring out how to optimize is only half the battle. The other half is figuring out what to optimize in the first place. And the key to that lies with us, people – what we want is the very definition of the cost function. My work casts the process of the robot acquiring its cost function as a human-robot collaboration, introducing theory and tools for aligning robot incentives with human preferences and ensuring that robots are resilient to changes in their environment.
Gerhard Neumann is a Professor of Robotics & Autonomous Systems at the University of Lincoln. Before coming to Lincoln, he has been an Assistant Professor at the TU Darmstadt from September 2014 to October 2016 and head of the Computational Learning for Autonomous Systems (CLAS) group. Before that, he was Post-Doc and Group Leader at the Intelligent Autonomous Systems Group
(IAS) also in Darmstadt under the guidance of Prof. Jan Peters. Gerhard obtained his Ph.D. under the supervision of Prof. Wolfgang Mass at the Graz University of Technology. Gerhard already authored 50+ peer reviewed papers, many of them in top ranked machine learning and robotics journals or conferences such as NIPS, ICML, ICRA, IROS, JMLR, Machine Learning and AURO. He is principle investigator for the National Center for Nuclear Robotics (NCNR) in Lincoln which is an EPSRC RAI Hub and also leading 1 Innovate UK project on Tomato Picking. In Darmstadt, he is principle investigator of the EU H2020 project Romans. He organized several workshops and is senior program committee for several conferences.
Talk # 1
Information-Geometric Policy Search for Learning Versatile, Reusable Skills
In the future, autonomous robots will be used for various applications such as autonomous farming, handling dangerous materials as for example decommissioning nuclear waste, health care or autonomous transportation. For such complex scenarios, it is inevitable that autonomous robots are equipped with sophisticated learning capabilities which enable it to learn from human teachers as well as from self-improvement. In this talk, I will present our work on information-geometric policy search methods for learning complex motor skills. Our algorithms use information-geometric insights to exploit curvature and path information in order to perform efficient local search at the level of single elemental motions, also called movement primitives. Simultaneously to local search, the algorithms search on a global level by selecting between distinct solutions, allowing us to represent a versatile solution space with high quality solutions. Our algorithms can be used to efficiently learn motor skills, generalize these motions to different situations, learn reactive skills that can react to perturbations and select and learn when to switch between these motions. I will also briefly show how to extend our algorithms to learn from preference-based feedback instead of a numeric reward signal, enabling a human expert to guide the learning agent without the need for manual reward tuning. While I will use dynamic motor games, such as table tennis, as motivation throughout my talk, I will also shortly present how to apply similar methods for robot grasping and manipulation tasks.
Cheonan, South Korea
Ji-Hun Bae received the B.S. and M.S. degrees in electrical engineering from Myongji University, South Korea, in 1999 and 2001, respectively, and the Ph.D. degree in robotics from Ritsumeikan University, Japan, in 2004. He was a Postdoctoral Fellow of the JSPS 21st Century COE Program at Ritsumeikan University, Japan. He was a Senior Researcher at KIRO, from 2006 to 2008, and a Postdoctoral Fellow at KIST, from 2008 to 2009. He is currently a Principal Research Scientist in the Robotics R&D Group, Korea Institute of Industrial Technology, South Korea. His research interests include control algorithms for robotic hands and arms, coordinated hand–arm control, bimanual manipulation, and robotic assembly.
Talk # 1
Development and Usage of Allegro Hand
Recently, there has been a growing interest in grasping technology and gripper/hand for object manipulation due to the demand for manipulation technology in the field of industrial robots. In this talk, I would like to talk about ‘Allegro hand’, a representative robot hand that has been used as a research platform in various research institutes. As a designer of the ‘Allegro hand’, I talk about the development purpose, process, usage, and future improvement direction of the hand.
Marc Toussaint is full professor for Machine Learning and Robotics at the University of Stuttgart since 2012. In 2017/18 he spend a year as visiting scholar at MIT, and before that some months with Amazon Robotics. His research focuses on the combination of decision theory and machine learning, motivated by fundamental research questions in robotics. Reoccurring themes in his research are appropriate representations (symbols, temporal abstractions, relational representations) to enable efficient learning and manipulation in real world environments, and how to achieve jointly geometric, logic and probabilistic learning and reasoning. He currently is coordinator of the German research priority programme Autonomous Learning, member of the editorial board of the Journal of AI Research (JAIR), reviewer for the German Research Foundation, and programme committee member of several top conferences in the field (UAI, R:SS, ICRA, IROS, AIStats, ICML). His work was awarded best paper at R:SS'18, ICMLA'07 and runner up at R:SS'12, UAI'08.
Talk # 1
Physical Reasoning & Robot Manipulation
There recently is, again, substantial optimism about AI due to the great advances in machine learning and data-driven methods. However, a core challenge remains to capture and formalize essential structure in real-world decision making and control, and thereby provide the foundation for sample-efficient learning and strong generalization. To this end we need a united machine learning, probabilistic AI and robotics research approach. In this talk I will summarize my work on learning and reasoning in logic, geometric and uncertain domains, and highlight what I think are core research challenges towards real-world AI.
Stéphane Dauzere-Peres is Professor at the Center of Microelectronics in Provence (CMP) of Mines Saint-Etienne in France and Adjunct Professor at BI Norwegian Business School in Norway. He received the Ph.D. degree from the Paul Sabatier University in Toulouse, France, in 1992; and the H.D.R. from the Pierre and Marie Curie University, Paris, France, in 1998. He was a Postdoctoral Fellow at the M.I.T., U.S.A., in 1992 and 1993, and Research Scientist at Erasmus University Rotterdam, The Netherlands, in 1994. He has been Associate Professor and Professor from 1994 to 2004 at the Ecole des Mines de Nantes in France. He was invited Professor at the Norwegian School of Economics and Business Administration, Bergen, Norway, in 1999. His research interests broadly include modeling and optimization of operations at various decision levels (from real-time to strategic) in manufacturing and logistics, with a special emphasis on semiconductor manufacturing. He has published more than 65 papers in international journals. He has coordinated multiple academic and industrial research projects. He was runner-up in 2006 of the Franz Edelman Award Competition, and won the Best Applied Paper of the Winter Simulation Conference in 2013.
Talk # 1
Some Challenges on Integration of Decisions in Logistics
In this talk, we first present the notions of horizontal integration and vertical integration of decisions in logistics, with their main characteristics and motivations. Challenges related to each integration type are then discussed using examples based on academic and industrial research conducted by the author. The integration of decisions in production planning and scheduling and a railway transportation example are discussed for vertical integration. A production planning and vehicle routing problem and a maritime supply chain example are presented for horizontal integration.
Talk # 2
Achievements And Lessons Learned From A Long-Term Academic-Industrial Collaboration
I had the opportunity to work for about 14 years on many different projects with two manufacturing sites of the French-Italian semiconductor company STMicroelectronics. Supported by European, national and industrial projects, this still active long-term academic-industrial collaboration led to many scientific and industrial achievements, spreading to other companies. Through regular exchanges, engineers, researchers, PhD and Master students were able to present their problems, their advances and generate new research projects. After some history of the collaboration, the presentation will survey some of the main research and industrial results in qualification and flexibility management, production and capacity planning, scheduling, automated transportation, dynamic sampling and time constraint management. Challenges faced and lessons learned when applying Operations Research and Industrial Engineering in practice, and in particular in semiconductor manufacturing, will be discussed. Benefits for both practitioners and researchers will be emphasized, such as the opportunity to propose and study new relevant problems and develop and apply novel approaches using actual industrial data.
Tempe (AZ), USA
John Fowler is the Motorola Professor of Supply Chain Management in the W.P. Carey School of Business at Arizona State University. He served as the department chair of supply chain management from 2011-2016. Prior to that he was the Avnet Professor of Industrial Engineering at ASU. His research interests include discrete event simulation, deterministic scheduling, and multi-criteria decision making. He has published more than 120 journal articles and more than 100 conference papers. Professor Fowler recently served as editor-in-chief for IIE Transactions on Healthcare Systems Engineering and continues to be the department editor for Healthcare Operations Management at the journal. He is an editor of the Journal of Simulation and associate editor of IEEE Transactions on Semiconductor Manufacturing. He is a fellow of the Institute of Industrial and Systems Engineers and recently served as the vice president for continuing education. He is a former INFORMS vice president, and served on the Winter Simulation Conference board of directors from 2005-2017. He was also the program chair for the 2002 and 2008 Industrial Engineering Research Conferences and the 2008 Winter Simulation Conference.
Talk # 1
A Framework for Designing Remote Diagnostics Networks for Equipment Suppliers
With advances in information technology, service activities for expensive equipment used in semiconductor manufacturing can be performed from a remote location. This capability is called remote diagnostics (RD). RD has the potential to reduce maintenance and capital costs and improve productivity. In this presentation, we develop a queueing-location model to analyze the capacity and location problem of after sales service providers, considering the effects of RD technology. Our model optimizes the location, capacity and the type of service centers while taking congestion effects into consideration. We solve this model using a simulation optimization approach in which we use a genetic algorithm to search the solution space. We demonstrate how our methodology can be used in strategic investment planning regarding the adoption of RD technology and service center siting through a realistic case study.
Talk # 2
An Overview of Scheduling with Batch Processing Machines in Semiconductor Manufacturing
Batch processing machines play an important role in the operational performance of semiconductor manufacturing factories, both wafer fabs and assembly/test facilities. In this context, a batch is a set of lots that are processed at the same time on a single machine. Not only is there a trade-off between the time spent by lots waiting to form a batch versus the time lots spend waiting for a machine to become available (based on running partial loads), but the amount of variability introduced by various approaches to forming batches and scheduling them can make a large difference on the performance of these machines as well as downstream machines (and thus the factory performance). In this paper we will focus on situations where the time to process a batch is either a constant based on the recipe (so called incompatible families) or the maximum time required by any of the lots in the batch (so called compatible families). Together these are known as p-batch scheduling. The primary focus of paper will be on offline, deterministic models of semiconductor machine environments that include at least one batch processor (single machine, parallel machines, flowshops, and jobshops) and will discuss both the real world situations these models represent in semiconductor manufacturing and what solutions techniques are used.
Young Jae Jang received his Ph.D. degree in mechanical engineering from MIT in 2007 and a double M.S. degree in mechanical engineering and operations research from MIT in 2001. He received a B.S. degree in aerospace engineering from Boston University in 1997. He is currently an Associate Professor in the Industrial and Systems Engineering Department at KAIST, South Korea. His current research includes automated material handling systems (AMHS) design, and system design and analysis particularly for wireless power transfer–based logistics systems. He has been actively working with companies including Samsung Electronics, LG Electronics, and Samsung Heavy Industry. In early 2018, he received a USD$2 million research grant from Shinsung Factory Automation Ltd., one of the largest global AMHS solution providers in the semiconductor industry, and founded the Shinsung-KAIST AI AMHS Research Center at KAIST. As Director of the official KAIST research center, he is responsible for commercializing the new AMHS control system for semiconductor fabs using the learning-based vehicle routing and dispatching algorithms developed by his research group. Dr. Jang was the Co-Chair of the 2015 International Symposium on Semiconductor Manufacturing Intelligence (ISMI). He is currently the Guest Editor of a Special Issue on “Artificial Intelligence in Manufacturing and Logistics Systems: Algorithms, Applications, and Case Studies,” for International Journal of Production Research. Dr. Jang is also currently an Associate Editor of Computers & Industrial Engineering.
Talk # 1
Deep Q-Learning Based Semiconductor AMHS System Design and Industry Case Study
The learning based dynamic routing algorithm is proposed for the overhead hoist transport (OHT) system for semiconductor fabrication facility (FAB). The OHT system, which consists of multiple vehicles moving at high speeds on guided rails, is the primary automated material handling system (AMHS) in FABs. Modern large-scale FABs have hundreds of vehicles delivering lots between processing machines. The dynamic routing method is the route guidance method that dynamically selects the vehicles’ paths by considering the conditions of traffic and congestion. We develop a reinforcement learning-based dynamic routing algorithm called
QLBWR($\lambda$), which consists of a dynamic Boltzmann softmax policy and reward shaping on a Q($\lambda$) learning method. The proposed algorithm uses real-time information to effectively guide each vehicle so that it avoids congestion and finds its optimal path. The algorithm is also designed such that the computational burden to find its optimal route is significantly low enough to serve hundreds of vehicles in real time. The performance of the proposed algorithm is compared with various static and dynamic algorithms with simulation analyses on an actual FAB layout. The results show that the algorithm outperforms and is superior to the other benchmarking algorithms.
Talk # 2
Dynamic scheduling of the dual stocker system using reinforcement learning
The stocker system is the most widely used material handling system in LCD and flat panel fabrication facilities (FABs). The stocker mainly consists of one or two cranes moving along a single track to transport lots, or cassettes, containing 10 to 30 thin glass substrates between processing machines. Because the stocker system is the primary material handling system in the FABs, its performance directly affects the overall performance. In this study, we investigate the scheduling of a dual stocker system operating with two cranes simultaneously on a single track and propose a learning-based scheduling algorithm for the system. We report some of the results of our long-term efforts to dynamically optimize the dual-crane stocker. We show the modeling and algorithm to minimize the make-span of the jobs. In particular, we incorporate a reinforcement learning method in the scheduling algorithm. The model is validated in an extensive simulation study based on actual data.
(Samuel) Qing-Shan Jia received his B.E. and Ph.D. degrees in automation and control science and engineering from Tsinghua University, Beijing, China in 2002 and 2006, respectively. He is a tenured Associate Professor in the Center for Intelligent and Networked Systems, Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University. He was a visiting scholar at Harvard, HKUST, and MIT in 2006, 2010, and 2013, respectively. His research interest is to develop an integrated data-driven, statistical, and computational optimization approach for cyber physical systems with applications to smart buildings and energy Internet. He is an associate editor (AE) of IEEE Transactions on Automatic Control, and was an AE of IEEE Transactions on Automation Science and Engineering and J. of Discrete Event Dynamic Systems. He chairs the Control for Smart Cities Technical Committee in IFAC and the Beijing Chapter Chair of IEEE Control Systems Society, and co-chairs the Smart Buildings Technical Committee in IEEE Robotics and Automation Society. He chaired the Discrete Event Systems Technical Committee in IEEE Control Systems Society. He is a member of the Technical Committee on Control Theory and the Technical Committee on Information Security of Industrial Systems in the Chinese Automation Association.
Talk # 1
Artificial Intelligence in Cyber Physical Energy Systems - Event-Based Learning and Optimization
Cyber physical energy system (CPES) is where information and energy merges together to improve the overall system performance including economic, comfort, and safety aspects. Artificial intelligence which are enabled by internet of things, big data, and cloud computing, has a big role in the optimization of CPES. In this talk, we focus on a real problem in smart buildings, in which multiple buildings are connected into a micro grid. The renewable energy such as solar power and wind power are generated locally in the building, stored in the building, and consumed in the building by plug-in loads and electric vehicles. There are models to predict the power generation and consumption in minutes, hours, and days. And there are models to predict the power generation and consumption in individual buildings or a group of buildings. We developed a multi-scale event-based reinforcement learning method which makes decisions only when certain events occur, and uses policy projection and state and action aggregation to connect the models in multiple scales. The performance of this method is demonstrated by numerical examples. We will also discuss extensions of this method to distributed optimization. We hope this work sheds light to the optimization of CPES.
Talk # 2
Title: Event-Based Learning for Smart Buildings – from Energy Saving to Fast Evacuation
Building is responsible for nearly 40% of energy consumption in many developed and developing countries around the globe. Recent technology advances have enabled the deployment of small sensors and actuators within the building, the wearable devices for the occupants, and the human machine interface for convenient interaction between the occupants and the buildings. It is of great practical interest to develop scalable methods to handle the large amount of data from these sensors and to provide real-time decision making for energy saving and fast evacuation in smart buildings. We focus on this important problem in this talk and show two examples. The first example considers energy saving in buildings. After reviewing the state of the art in this field, we will show a distributed event-based learning method for estimating the distribution of occupants within the building. This method uses multi-sensor fusion and uses stay-time to reduce the accumulative estimation error. The second example considers fast evacuation in buildings. Exploring the structural property of the problem, we will show approaches for fast modeling, indoor localization, and navigation and evacuation guidance for fire fighters. We hope these examples may attract more researchers to join the field of smart buildings.
Seoul, South Korea
Kyu-Jin Cho received B.S and M.S. degrees from Seoul National University, Korea and a Ph.D. degree in ME from M.I.T. He was a post-doctoral fellow at Harvard Microrobotics Laboratory. At present, he is a professor of Mechanical and Engineering and the director of Soft Robotics Research Center and Biorobotics Laboratory at Seoul National University. He has been exploring novel soft robot designs, including a water jumping robot, origami robots and a soft wearable robot for the hand, called Exo-Glove Poly. The work on the water jumping robot was published in SCIENCE and covered by over 300 news media world-wide.
Talk # 1
Soft Robotics: A new paradigm for robotics research
Soft robotics is an emerging field of research that uses soft or compliant materials and elements to overcome the limitation of traditional robotics. Traditionally, robots have been used in an industrial environment with few unknown parameters. As more and more robots are used to interact with environments that are uncertain and vulnerable to change, a technology that can easily adapt to the changing environment is needed. Soft robotics deals with this issue by using soft and compliant elements in an intelligent way. In this talk, I will describe several novel robotic systems that uses softness to achieve shape control or stiffness control to provide a safe, lightweight and hopefully low cost solution. The key design principle is embodied intelligence or morphological computation which can reduce the complexity of the system while providing useful functionality.
Talk # 2
Soft Robots with Physically Embodied Intelligence
Soft robotics deals with interaction with environments that are uncertain and vulnerable to change, by easily adapting to the environment with soft materials. However, softness requires controlling large degrees of freedom. Many soft robots use pneumatics which can easily distribute the actuation. If tendons are used for actuating a soft body, the large degrees of freedom of the material either requires large number of tendons or limits the controllability. Tendon drive soft robots can benefit from using the concept of physically embodied intelligence, first proposed by Prof. Rolf Pfeifer. By embodying intelligence into the design, better performance can be achieved with a simpler actuation. In nature, there are few example that exhibit this property. Flytrap, for example, can close its leaves quickly by using bistability of the leaves instead of just relying on the actuation. Inchworm achieves adaptive gripping with its prolegs by using the buckling effect. In this talk, I will give an overview of various soft robotic technologies, and some of the soft robots with physically embodied intelligence that are being developed at SNU. These examples will show that the concept of physically embodied intelligence simplifies the design and enables better performance by exploiting the characteristics of the material.
Fumiya Iida (SM’17) is a university lecturer at Department of Engineering, University of Cambridge. He is also the director of Biologically Inspired Robotics Laboratory and a fellow of Corpus Christi College. He received his bachelor and master degrees in mechanical engineering at Tokyo University of Science in Japan, and Dr. sc. nat. in Informatics at University of Zurich in Switzerland. During his PhD project, he was also engaged in biomechanics research of human locomotion at Locomotion Laboratory, University of Jena in Germany. While he worked as a postdoctoral associate at the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology in USA, he awarded the Fellowship for Prospective Researchers from the Swiss National Science Foundation, and then, the Swiss National Science Foundation Professorship hosted by ETH Zurich. In 2014 he moved to the University of Cambridge as the director of Bio-Inspired Robotics Laboratory. His research interest includes biologically inspired robotics, embodied artificial intelligence, and soft robotics, where he was involved in a number of research projects related to robot locomotion, manipulation, and human-robot interactions leading to some start-up companies. He was a recipient of the IROS2016 Fukuda Young Professional Award, and Royal Society Translation Award in 2017.
Talk # 1
Biologically Inspired Soft Robotics: Challenges and Perspectives
As the complexity of robotic systems enhances, it becomes increasingly more difficult for humans to design and construct them manually. In particular, soft deformable systems that have essentially infinite design parameters as well as degrees of freedom, are often intractable for humans to apply conventional design and fabrication methods. For this problem, we have been exploring a set of alternative technologies to design and construct complex soft robots, such as multi-material 3D printing, electrically conductive elastomers, and model-free design automation processes. With the recent rapid progress of these technologies, we are able to develop a family of new robots that we can characterised as “morphologically computing machines”. We are exploring how such a new paradigm of design processes can be realised, though there are a number of known challenges such as the dimensionality problem, the scalability problem, and the reality gap.
Jumpei Arata is currently an Associate Professor in Department of Mechanical Engineering, Faculty of Engineering, Kyushu University, and a Guest Associate Professor in Department of Mechanical Engineering, Faculty of Engineering, the University of Tokyo, and was a Guest Professor in ETH Zurich in a short period (3 months) in 2010. He received M. Eng. degree in Shibaura Institute of Technology and Ph. D. degree in the University of Tokyo. During his studies in Master course and Ph.D, he had been an exchange student and a Swiss Federal scholarship student in the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, in 1997-1998 and 1999-2000 respectively. He has been working on Medical Robotics, specifically, Surgical Robot since his study in M. Eng. Through his Ph.D thesis, he build a tele-surgical robotic system and conducted several times of in-vivo tele-surgery experiments between Japan-Korea and Japan-Thailand. He is currently working on a surgical robot using flexible structure, that can miniaturize robotic forceps with 4 degree-of-freedom at the tip to the diameter of 2 mm. His research interests include Surgical Robot, Rehabilitation Robot, Parallel Mechanism, Soft Robotics, Compliant Mechanism and Haptics. His research work introduction, publications can be found in: http://system.mech.kyushu-u.ac.jp/member-arata.html
Talk # 1
Synthetic Elastic Mechanism - Neurosurgical Robotic Forceps within 2 mm in diameter
Clinical demands for minimal invasiveness in surgery is increasing, as it is beneficial for both patients and medical economy. Surgical robots have been widely studied for their ability to perform minimally invasive surgery. The miniaturized robotic tools play a key role in reducing the size of the wound while providing multiple degrees-of-freedom (DOF) inside the body cavity for performing dexterous motions required for the surgery. Many studies have been conducted on miniaturized surgical robotic tools, and most of the mechanisms have been developed based on wire, link, and pneumatic mechanisms. Here, we propose a surgical robotic tool using a synthetic elastic beam structure that is largely deformed during motion to transmit and transform the motion at the tip of the miniaturized surgical tool. The elastic mechanism enables further compaction and simplicity, as an elastic structure requires a lesser number of mechanical parts than that required in the other mechanisms. Recently, we succeeded to implement a prototype within the diameter of 2mm and 4 DOF at the tip. At the best of our knowledge, the developed prototype is the world smallest robotic forceps with 4 DOF at the tip. We implemented the robotic forceps for transnasal endoscopic pituitary tumor resection that requires a fine manipulation in a deep and confined area.
Talk # 2
What is “intuitiveness” in surgical robot? An attempt to increase the usability by inducing illusions
Intuitiveness in robotic surgery is highly desirable when performing highly elaborate surgical tasks using surgical master–slave systems (MSSs), such as suturing. To increase the operability of such systems, the time delay of the system response, haptic feedback, and eye–hand coordination are the issues that have received the most attention. In addition to these approaches, we propose a surgical robotic system that induces a multisensory illusion. In our previous study, we reported that a robotic instrument we devised enhances the multisensory illusion in a traditional rubber hand illusion. By having inspired this finding, we introduce the same concept to a multi-degree-of-freedom surgical robotic system for a further intuitive control. One of the biggest challenges is that the appearance and response time of the system are found to be key factors for improved intuitiveness. We thus developed a surgical robotic system with a wearable master and a compact slave system. Further, we recently developed a miniaturized hand that has 15 degree-of-freedom with 10 mm in diameter, and the integration is currently on-going. We hope to present the results soon in a conference.
Prof. Jessica Burgner-Kahrs is founder and director of the Laboratory for Continuum Robotics at Leibniz Universität Hannover, Germany since November 2015. She graduated from Universität Karlsruhe
(TH), Germany in computer science and awarded a doctoral degree at Karlsruhe Institute of Technology (KIT), Germany. Before she started at Leibniz Universität Hannover in 2013, she was Research Associate at Vanderbilt University, USA for two years. Her research focus lies on small-scale continuum robotics, particularly their design, modeling, planning and control as well as human-robot interaction. Applications range from minimally invasive surgery to maintenance, repair, and operations of capital goods. In 2015, she was awarded with the Heinz Maier-Leibnitz Prize, the Lower Saxony Science Award in the category Young Researcher and entitled Young Researcher of the Year 2015 in Germany. The Berlin-Brandenburg Academy of Sciences awarded her the Engineering Science Prize in 2016. She was elected among the Top 40 under 40 in the category Science and Society in 2015, 2016 and 2017 by the business magazine Capital.
Talk # 1
Continuum Robots - Developments and Challenges on a Millimeter Scale
Continuum robots are not composed of discrete joints or rigid links and thus differ substantially from conventional robots. Their structure is inspired by nature, in particular by the animal kingdom, e.g. elephant trunks, anteater tongues, or tentacles. Continuum robots are composed of flexible, elastic, or soft materials such that they can exhibit complex bending motions and achieve dexterous manipulation even in constrained environments. The high scalability and miniaturization potential allow for numerous applications, e.g. minimally invasive surgery through natural orifices or inspection of capital goods such as aircraft engines. The presentation gives an overview on continuum robot designs and touch upon fundamentals in kinematic modeling, planning and control. The merits of continuum robots are discussed for example applications and open research questions and challenges are elaborated.
Talk # 2
Computational Challenges and Applications of Continuum Robotics
Continuum robots are composed of flexible, elastic, or soft materials such that they can exhibit complex bending motions and achieve dexterous manipulation even in constrained environments. The high miniaturization potential allows for numerous applications, e.g. minimally invasive surgery through natural orifices or inspection of capital goods such as aircraft engines. Computational challenges are associated with the highly nonlinear kinematics, workspace characterization, as well as planning and control. After a general introduction to continuum robotics, the talk focusses on kinematic modeling using methods from differential geometry and elasticity theory and on elevating these concepts to planning problems. The talk concludes with future challenges and applications.
San Diego (CA), USA
Michael Yip is an Assistant Professor and the Director of the Advanced Robotics and Controls Lab at UCSD. His research focuses on three areas: (1) flexible robots for surgery, (2) visual computation for image-guided robots, and (3) robotic actuators for bionic devices. Recent efforts in his research group involve building, controlling, and automating endoscopic and catheter robots for treating heart and lung disease, designing artificial intelligence for robot-human collaboration in surgery, and augmenting surgeon teams with augmented reality for minimally invasive surgery. His work in flexible surgical robots and robot muscles have been nominated and have won best paper awards in major robotics conferences. Dr. Yip has been a visiting researcher at the Harvard University and MIT in the area of surgical robotics and tissue engineering, and a research associate with Walt Disney Research in Los Angeles working on next-generation animatronics. He received a Bachelors in Mechatronics Engineering from the University of Waterloo, a Masters in Electrical Engineering from the University of British Columbia, and a Ph.D. in Bioengineering from Stanford University.
Talk # 1
Learning to control and plan surgical robots within the body
Surgical robotics offers an unprecedented ability to place and dexterously control small robotic instruments, immersive stereo imaging and other sensing modalities deep within inaccessible locations in the body. This presents major opportunities to in the medical domain to treat diseases (e.g. cardiac arrhythmia, lung cancer, colon cancer) in a minimally invasive fashion beyond. Yet, as these devices get smaller, more flexible and more mechanically complex, we are presented with a new challenge: do we rely on the doctor to sort out the challenging control of the devices while simultaneously processing the multi-modal biosignals from onboard sensing? Or do we off-load the low-level control of the surgery from human teleoperation onto a semi-autonomous or fully-autonomous framework? I will discuss our work in developing robot-assisted surgeries that analyze a multimodal spectrum of sensory information, physics models, and imaging information in real-time to optimally plan and perform semi-autonomous surgery. This includes real-time learning-based controllers for automating catheter and endoscopic robots within difficult anatomy, modular snake-like devices for efficient locomotion in difficult environments, visual computation methods for image-guided robotics, and robot intelligence for robot-human teams. Finally, I will discuss directions we aim to pursue in reinforcement learning such that with limited self-training, our robot-assistive devices learn to become expert robot surgeons.
Talk # 2
Learning Model-free Representations for Fast, Adaptive Robot Control and Motion Planning
Robot manipulation has traditionally been a problem of solving model-based control and motion planning in structured environments. This has made them very well suited for a finite set of repeating tasks and trajectories such on a manufacturing assembly line. However, when considering more complex and partially-observable environments, and when more complex and compliant and safe robots are proposed, outcomes of robot actions become more and more uncertain, and model-based methods tend to fail or produce unexpected results. Erratic behavior makes robots dangerous in human environments and thus new approaches must be taken. In this talk, I will discuss our research in learning model-free representations for robots that enable robots to learn and adapt their control to new environments and conditions, and perform fast motion planning and adaptation to changing environments. These representations are trained using a variety of local and global model-free learning strategies, and when implemented are comparatively significantly faster, more consistent, and more power and memory efficient than conventional control and trajectory planners. I will relate its relevance to surgical robotics and a new frontier in autonomous surgery.