- Education, Outreach and Career
- Distinguished Lecturer Program
- Distinguished Lecturers by Name
Distinguished Lecturers by Name
Talk #1Ergonomic human-robot interaction and collaboration
This talk will present the new concept of ergonomic human-robot interaction and collaboration. In the first part, an overview of the reduced-complexity human dynamic modeling and the associated ergonomic factors such as overloading torques, body compressive forces, fatigue, will be presented. Next part will focus on the control of human-robot interaction and collaboration, through an optimization problem that takes into account the ergonomic requirements of the human co-worker. Using the reduced-complexity whole-body dynamic model of the human, the position of the interactive/collaborative tasks in the workspace will be optimized. In this configuration, the ergonomic indexes, such as the overloading torques, i.e. the effects of an external load in human body joints, are minimized. In addition, the optimization process includes several constraints, such as human arm manipulability properties, to ensure that the human has a good manipulation capacity in the given configuration. The main advantage of this approach is that the robot can potentially help to reduce the work-related strain and increase the productivity of the human co-worker.
Talk #2A novel framework of context-aware and adaptive interaction between the robot and uncertain environments
Nowadays, robots are expected to enter various application scenarios and interact with unknown and dynamically changing environments. This highlights the need for creating autonomous robot behaviors to explore such environments, identify their characteristics and adapt, and build knowledge for future interactions. To respond to this need, this talk presents a novel framework that integrates multiple components to achieve a contextaware and adaptive interaction between the robot and uncertain environments. The core of this framework is a novel self-tuning impedance controller that regulates robot quasi-static parameters, i.e., stiffness and damping, based on the robot sensory data and vision. The tuning of the parameters is achieved only in the direction(s) of interaction or movement, by distinguishing expected interactions from external disturbances. A vision module is developed to recognize the environmental characteristics and to associate them to the previously/newly identified interaction parameters, with the robot always being able to adapt to new changes or unexpected situations. This enables a faster robot adaptability, starting from better initial interaction parameters. The application of this framework in various interaction scenarios such as soft material handling, item sorting, load carrying, will be presented.
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.
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.
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.
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.
Yasemin is an Assistant Professor in the Automatic Control group at Chalmers University of Technology and Senior Research Fellow in Statistical Machine Learning Group at University College London. She completed her Ph.D. at the Royal Institute of Technology (KTH), Sweden, in 2012. As a researcher at KTH, she was involved in the EU projects CogX(Cognitive Systems that Self-Understand and Self-Extend) and RoboHow (Web-enabled and Experience-based Cognitive Robots that Learn Complex Everyday Manipulation Tasks). Later, she worked as a post-doctoral researcher at University of Birmingham contributing to the EU project RoMaNs (Robotic Manipulation for Nuclear Sort and Segregation), and as a research scientist at ABB, Corporate Research, Sweden, coordinating the EU project SARAFun (Smart Assembly Robot with Advanced Functionalities), and as a roboticist at Vicarious AI, CA, USA, leading R&D activities on robotic grasp planning for industrial tasks. Her research is focused on data efficient learning from multisensory data for robotic grasping and manipulation applications. She received the Best Paper Award at IEEE International Conference on Robotics and Automation for Humanitarian Applications (RAHA) in 2016 and the Best Manipulation Paper Award at IEEE ICRA in 2013, and was IEEE/RSJ IROS CoTeSys Cognitive Robotics Best Paper Award Finalist in 2013.
Talk #1: Learning from multi-modal data for grasping
The central question of my research is how we can create robots that are capable of adapting such that they can co-inhabit our world. This means designing systems that are capable of functioning in unstructured environments that are continuously changing with unlimited combinations of shapes, sizes, appearance, and positions of objects; adapting to changes and learning from experience and humans, and importantly do so from small amounts of data. In specific, my work focuses on grasping and manipulation, fundamental aspects to enable a robot to interact with the environment, humans and other agents, along with dexterity (e.g. to use objects/tools successfully) and high-level reasoning (e.g. to decide about which object/tool to use). Despite decades of research, robust autonomous grasping and manipulation at a level approaching human skills remains an elusive goal. One main difficulty lies in dealing with the inevitable uncertainties in how a robot perceives the world, based on sensory measurements that can be noisy and incomplete, which poses challenges to avoid failures. In my research I study how to enable a robot to interact with natural objects and learn about object properties and relations between tasks and sensory streams. I develop tools that allow a robot to use multiple streams of sensory data in a complementary fashion. In this talk I will address how a robot can use sensory data, e.g. visual and tactile, in various steps involved to achieve robot grasping and manipulation tasks.
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.
Prof. Davide Brugali graduated in Electronic Engineering at the Politecnico di Milano in 1994; he received the PhD in Computer Science from the Politecnico di Torino in 1998. From 2001 until 2011 he was Assistant Professor at the University of Bergamo. Since 2011 he has been Associate Professor at the Department of Engineering of the University of Bergamo.
He was visiting researcher at the CMU Robotics Institute for one year between 1997 and 1998 and visiting professor at NASA Jet Propulsion Laboratory in 2006. He served as Co-Chair of the IEEE RAS Technical Committee on "Software Engineering for Robotics and Automation" from 2000 to 2019, as Associate Editor of the IEEE Robotics and Automation Magazine from 2009 to 2011 and Editor-in-Chief of the Journal of Software Engineering for Robotics from 2009 to 2018. He is main author of the book "Software Development - Case Studies in Java" published by Addison-Wesley in 2005.
He is the coordinator of the Robotics Laboratory of the University of Bergamo. His research activity focuses mainly on software engineering methodologies and techniques for the development of robot control systems and applications.
Talk 1: Software Variability in Service Robots Architectures
Robots of the 21st century are versatile machines with the potential to enhance transportation safety, reduce agricultural pesticide use, and improve public safety and crime-fighting efficacy, among other things. Of course, cost is a significant barrier to advancing robotics and related product development, and it’s directly tied to the necessary complexity of software control systems. Such systems require enormous flexibility to easily accommodate volatile requirements or changing needs. To this end, the software control systems need to be customizable for different tasks, hardware, and operating environments. In the talk, I will report on a recent investigation on drivers, practices, methods, and challenges of software variability both from the state of the art in robotics research and from industrial companies building service robots. I will report observations emerged from this investigation, formulate hypotheses trying to explain these observations, and provide actionable recommendations for researchers, tool providers, and practitioners.
Talk 2 : Runtime reconfiguration of robot control systems
Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems. They exploit knowledge about the environment to trigger runtime adaptation so that they exhibit a behavior adequate to the current context: they adapt themselves to changes in their execution environment and internal dynamics, such as response to failure, variability in available resources, or changing tasks. Context-aware self-adaptation consists in being able to dynamically reconfigure the software architecture (i.e. activating/deactivating components, changing their connections, etc.) and adapting the system behavior (i.e. updating a sensor scanning rate, replacing the localization algorithm) in order to exploit at best the robot hardware and software resources in every operational conditions. In the talk, I will illustrate software design guidelines for the development of self-reconfigurable
Toronto (ON), Canada
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.
Fei Chen received the B.S. in computer science from Xi’an Jiaotong University in 2006, the M.S. in computer science from Harbin Institute of Technology in 2008, and the Dr. Eng. in robotics from Nagoya University, Japan, in 2012. Then he joined the Department of Advanced Robotics at the Italian Institute of Technology and found the Active Perception and Robot Interactive Learning laboratory. Since 2020, he has been an assistant professor with Department of Mechanical and Automation Engineering at The Chinese University of Hong Kong. He is also affiliated with CUHK T Stone Robotics Institute and Hong Kong Centre for Logistics Robotics. His research interests lie in robot learning and control for various types of mobile manipulation robots that collaborate with human beings. He serves as Associate Editor of IEEE Transactions on Cognitive and Developmental System. He is a senior member of IEEE.
Robot Mobile Manipulation: from Autonomous to Collaborative
Modern society demands the heavy usage of robotic mobile manipulators with high autonomy and intelligence to work ether independently or collaboratively with human beings. For this purpose, the robot should be able to accomplish various manipulation tasks without or will little prior knowledge of the status of objects and human workers in a highly unstructured and dynamic environment. It is important first for the mobile manipulation robots to understand human status (intention, cognitive status, etc.) via multi- modal sensing means and then achieve human level capability in terms of perception of environment, planning and learning of manipulation skills adaptively and intelligently. In this talk, we will cover various topics and issues along the direction to develop collaborative mobile manipulation robots the team has been achieved in the past years and demonstrate various setup of autonomous mobile manipulator for various flexible domestic and industrial scenarios.
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.
Anibal De Almeida
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.
New Haven (CT), USA
Aaron Dollar is a Professor of Mechanical Engineering & Materials Science and Computer Science at Yale University, where he has been on faculty since 2009. He earned a B.S. in Mechanical Engineering at the University of Massachusetts at Amherst, S.M. and Ph.D. degrees in Engineering Sciences at Harvard, and conducted two years of Postdoctoral research at the MIT Media Lab. Professor Dollar directs the Yale GRAB Lab, with research primarily focused on human and robotic grasping and dexterous manipulation, mechanisms and machine design, and upper-limb prosthetics. He has received a number of best paper and other prestigious awards, including junior faculty awards from NASA, DARPA, AFOSR, and NSF. His service to the Robotics research community includes the YCB benchmarking initiatives, the Yale OpenHand Project, OpenRobotHardware.org, RoboticsCourseware.org, and founding the IEEE RAS TC on Robotic Mechanisms and Design. His work on robotic grasping and manipulation focuses primarily on the mechanics of the problem, including contacts, passive and active degrees of freedom, and other constraints, and how proper focus on those, combined with clever mechanical design can facilitate excellent performance with even minimal sensing and control.
Talk #1: “Mechanical Intelligence” in Robotic Manipulation: Towards Human-level Dexterity in Robotic and Prosthetic Hands
The human hand is the pinnacle of dexterity – it has the ability to powerfully grasp a wide range of object sizes and shapes as well as delicately manipulate objects held within the fingertips. Current robotic and prosthetic systems, however, have only a fraction of that manual dexterity. In this talk, I will discuss how this gap can be addressed in three main ways: examining the mechanics and design of effective hands, studying biological hand function as inspiration and performance benchmarking, and developing novel control approaches that accommodate task uncertainty. I will place a special focus on examining the mechanics of the open- and closed-loop hand/object/environment system during robot manipulation and how contact and kinematic constraints dominate performance limits. Using this understanding, I will show how prioritizing passive mechanics in robot hand design, including incorporating adaptive underactuated transmissions and carefully tuned compliance, can maximize open-loop performance while minimizing complexity.
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.
Title of Talk #1
Reconsidering attitudes towards robots
The role of ambivalence Abstract of Talk #1 The Lecture will start out with a brief overview of existing works on attitudes towards robots in general and towards specific subtypes of robots, like service robots or education robots. This review will - at first glance - convey the impression that people ostensibly hold neutral to fairly positive attitudes towards robots. I will argue that this might be a measurement artefact, highlighting that indeed, attitudes towards robots can be characterized by ambivalence, rather than by neutrality. Recent evidence from our lab (Stapels & Eyssel, 2021) will exemplify this idea using novel measures to capture attitudinal ambivalence towards robots. From this follows: The way we measure a construct of interest undoubtedly impacts the resulting outcome. While this sounds trivial in the first place, it implies that a) we might want to reconsider the way we commonly assess attitudes towards social robots, and b) we might want to revisit and reassess existing results in light of the notion of ambivalence in attitudes towards robots.
Title of Talk #2
Diversity, Bias and social robots
The lecture will feature a social psychological perspective on the notion of diversity, with a specific focus on "gender" and social categorization in social robots. That is, I will outline core principles of human social cognition and demonstrate that these principles are likewise used in the context of nonhuman entities. To illustrate, in human-human social cognition, we readily apply fundamental dimensions of social cognition and social categories (e.g., traits like agency and communion or social categories like ethnicity, gender) to form judgments about individuals and groups. A set of empirical experiments will be presented to highlight the impact of design choices on the evaluation and behavior towards social robots. Implications for the notion of diversity in HRI and social robotics will be discussed.
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.
Hoboken (NJ), United States
Yi Guo joined the Department of Electrical and Computer Engineering at Stevens Institute of Technology in 2005, and is currently Thomas E. Hattrick Chair Professor. Before 2005, she was a Visiting Assistant Professor in the ECE Department of the University of Central Florida. Dr. Guo received her Ph.D. degree in Electrical and Information Engineering in 1999 from the University of Sydney, Australia. After her Ph.D, she worked as a postdoctoral research fellow at Oak Ridge National Laboratory for two years. Dr. Guo’s research interests are mainly in distributed and collaborative robotic systems, human-robot interaction, and dynamic systems and controls. She author one book, edited one book, and published over 150 journal and conference papers. She is currently the Editor-in-Chief of the IEEE Robotics and Automation Magazine. She has been Associate Editor of IEEE Robotics and Automation Letters, IEEE/ASME Transactions on Mechatronics, and IEEE Access. Her awards include Best Application Paper Award at WCICA2018, and Provost’s Award for Research Excellence from Stevens in 2020. She frequently gave invited talks and presented a Keynote Speech at IROS in 2019. She served in Organizing Committees of the premier robotics conference ICRA and IROS.
Robot-assisted guidance and regulation
The use of autonomous mobile robots in human environments is on the rise. Assistive robots have been seen in real-world environments, such as robot guides in airports, robot polices in public parks, and patrolling robots in supermarkets. In this talk, I will first present current research activities conducted in the Robotics and Automation Laboratory at Stevens. I’ll then focus on robot-assisted pedestrian regulation, where pedestrian flows are regulated and optimized through passive human-robot interaction. We design feedback motion control for a robot to efficiently interact with pedestrians to achieve desirable collective motion. Both adaptive dynamic programming and deep reinforcement learning methods are applied to the formulated problem of robot-assisted pedestrian flow optimization. Simulation results in a robot simulator show that our approach regulates pedestrian flows and achieves optimized outflow learning from the real-time observation of the pedestrian flow. Potential crowd disasters can be avoided as the critical crowd pressure is reduced by the proposed approach.
Decentralized cooperative control method for multi-robot formation
Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline of design, and the separation of perception and controls may cause processing latency and compounding errors that affect control performance. End-to end learning overcomes such limitation by learning directly from onboard sensing data, and outputs control command to robots. Challenges exist in end-to-end learning for multi-robot cooperative control and previous results are not scalable. We propose a novel decentralized cooperative control method for multi-robot formation using deep neural networks, in which inter- robot communication is modeled by a graph neural network. Our method takes the LIDAR sensor data as input, and the control policy is learned from demonstration provided by an expert controller in a decentralized way. While trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates triangulation formation behavior of multi-robot teams with varying sizes using the learned control policy.
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.
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.
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.
HeSuan Hu (M'11-SM'12) received the B.S. degree in computer engineering and the M.S. and Ph. D. degrees in Electro-mechanical Engineering from Xidian University, Xi'an, Shaanxi, China, in 2003, 2005, and 2010, respectively. He is a holder of more than 40 issued and filed patents in his fields of expertise. His current research interests include discrete event systems and their supervisory control techniques, Petri nets, automated manufacturing systems, multimedia streaming systems, autonomous vehicles, cyber security, and artificial intelligence. He has more than 140 publications in journals, book chapters, and conference proceedings in the above areas. Dr. Hu was a recipient of the many national and international awards, including the Franklin V. Taylor Memorial Award for Outstanding Papers from the IEEE SMC Society in 2010 and the finalists of the Best Automation Paper from the IEEE ICRA Society in 2013, 2016, and 2017. He has been an associate editor of the IEEE Control Systems Magazine, IEEE Robotics & Automation Magazine, IEEE Transactions on Automation Science and Engineering, IEEE Robotics & Automation Letters, Journal of Intelligent Manufacturing, etc.
Talk # 1: Distributed Supervisory Control Techniques for Automated Systems and Their Applications
In order to achieve their own destinations autonomously without disruption and downtime, large-scale automated systems must be well supervised and coordinated due to the competition for limited resources by many concurrent processes in various scenarios, e.g., Automated Logistics, Autonomous Vehicles, and Intelligent Manufacturing. Otherwise, severe problems may appear in terms of any kind of blockage issues. In the past decades, one may resort to monolithic resolution in the framework of centralized control as a conventional approach to optimal or acceptable solutions, but may suffer from computational difficulty. Thanks to the substantial improvement in algorithmic capability and computational capacity, some decentralized techniques are invented gradually, appearing more efficient when finding approximate solutions; nevertheless, most of them are application dependent. For the sake of its adaptive implementation in practice, we develop an innovative distributed approach. Subsystems are supervised locally so as to proceed concurrently and efficiently under the guidance of our control strategies, which can coordinate different processes in parallel by following real-timely generated and optimized trajectories. With the aid of this technique, each subsystem can operate autonomously to reach their destinations, being adaptive to increasingly complex scenarios. Global goals can always be achieved even with the absence of global information. For some critical advanced technologies or commercial applications, our technique provides not only distributed solutions but also formal guarantees for their behavioral correctness.
Talk # 2: Robustness Analysis and Implementation in Concurrent Systems with Unreliable Resources
Concurrent systems cover almost all indispensable infrastructure systems, e.g., Electricity Grids, Traffic Networks, Water/Gas Distribution Networks, Logistics Systems, and Automated Manufacturing Systems. In order to engage them in practice with their safety and/or security guaranteed, a prerequisite is that they must be immune to any undesirable behavior, e.g., stagnation and/or vulnerability, which have been extensively investigated for decades by numerous researchers. To ease their effort, resources are presumptively and arbitrarily assumed never to fail; nevertheless, this is quite the opposite as in reality. Actually, failures occur frequently due to various causes. Hereby, resource failures will be taken into consideration. In the paradigm of formal methods, a robust control supervisor is developed to guarantee that the system will not be in stagnation when failures occur unexpectedly. Processes not requiring the failed resources can continue their progress continually and smoothly. Various strategies are proposed to achieve the tractability in terms of robustness and concurrency among others. At each state, concurrent processes are executed in sequence so as to attain a set of feasible events. Anyone of them is legal to execute by obeying our strategies on line and in the real time. As a consequence, an appropriate event sequence is derived so as to lead the system to a desired state with an optimized trajectory. The eventual objective is to develop a not only feasible but also optimal solution which is effective in response to resource failures.
George Q. Huang
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.
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.
Qing-Shan (Samuel) Jia
(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.
Dr. Adar Kalir is Director of Manufacturing Science Systems within the Fab and Sort Manufacturing (FSM) network in Intel’s Manufacturing, Supply-chain and Operations (MSO) group. He is the lead engineer in FSM for Intel’s 10nm super-fin and Intel 7 products and is responsible for development of models and processes that enable factory productivity, output, cost, and cycle-time in startup, ramp, and high-volume manufacturing (HVM) across Intel's global manufacturing network. He has published over 110 technical papers, including IEEE Trans. on Semiconductor Manufacturing Best Paper Award in 2019. He also serves as an Adjunct Associate Professor at Ben-Gurion University (BGU), Israel, and as a Co-chair of the IEEE Technical Committee on Semiconductor Manufacturing Automation (TC- SMA).
On Manufacturing Science and Challenging Problems in Semiconductor Manufacturing
The semiconductor manufacturing process is likely the most complex manufacturing process that has ever been devised. It is characterized by extremely high cost of machines and equipment (~40%); very long production processes (100’s of process steps); continuously tighter control limits on inline parametrics, hence high variability (50% CoV’s > 2); high re-entrance (‘job-shop’ like) coupled with ‘flow line’ production management; and high mix of products. It is no surprise, therefore, that this environment also presents challenging problems to manufacturing practitioners, ranging from long-term strategic planning and supply-chain management, all the way down to challenging and uniquely constrained scheduling and dispatching problems at the shop-floor level. A few examples of such challenging problems shall be described – and practical implementable solutions to these problems will be reviewed. Specifically, the topics of production cycle time reduction via effective controls, opportunities for Preventive Maintenance (PM) improvements, and capital savings via optimal allocations of testing equipment are discussed as leading examples.
Maximizing Output During Ramp by Integrating Capacity and Velocity
In the semi-conductor industry, factories ramp their production capacity in parallel with tool (machine) installations. Ramp capacity, and subsequently wafer starts, are aligned with additional installs of the most expensive tool. Subsequently, starts are set to the full capacity of that limiting toolset. We argue that this may not be best method to maximize output over the ramp period and that by keeping ramp starts below constraint capacity, increased levels of cumulative output can be achieved for a desired planning horizon (e.g., the first year of production). A mathematical framework of the problem is provided to support our hypothesis, together with numerical examples and a set of simulation experiments.
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.
New Haven, (CT) USA
Rebecca Kramer-Bottiglio is an Assistant Professor of Mechanical Engineering and Materials Science at Yale University. She completed her B.S. at the Johns Hopkins University, M.S. at U.C. Berkeley, and Ph.D. at Harvard University. Prior to joining the faculty at Yale, she was an Assistant Professor of Mechanical Engineering at Purdue University. She currently serves as an Associate Editor of Soft Robotics, Frontiers in Robotics and AI: Soft Robotics, IEEE Robotics and Automation Letters, and Multifunctional Materials. She is the recipient of the NSF CAREER Award, the NASA Early Career Faculty Award, the AFOSR Young Investigator Award, the ONR Young Investigator Award, and was named to Forbes’ 2015 30 under 30 list.
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.
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.
Infrastructure Robotics – Autonomous Robots for Civil Infrastructure Maintenance
Maintaining civil infrastructure assets, including bridges, water main pipes, transmission towers and underwater structures, has been strictly constrained by humans’ limits and health/safety requirements. The need for safe, efficient and effective maintenance has led to a desire to automate maintenance operations. Intelligent robots that can operate either autonomously or collaboratively with humans in a complex infrastructure environment provide a very promising solution. However, developing autonomous robots for such application has a number of fundamental challenges. One is the way in which a robot moves and supports itself must be appropriate to the type of infrastructure. Another challenge is how the robot can operate autonomously because remote control of maintenance robots is not feasible due to the complexity of the environment and the difficulty of controlling motion in real-time. This talk will first discuss the functionalities robots should have for infrastructure maintenance. Research challenges and methodologies, including sensing, perception, planning, robot design and system integration, will then be discussed. A number of case studies of autonomous robots in practical industry applications will be presented, including autonomous robots for maintenance of steel bridges, power transmission towers and underwater structures, and robot teams for material and cargo handling.
Physical Human-Robot Collaboration (pHRC) – Research Challenges and Applications
Current applications of robotics is distinguished from more traditional automation by the focus on robots that operate autonomously in unstructured and dynamic environments, or collaboratively with humans. There has been increasing interest in the use of intelligent robots that can interact, assist and collaborate with humans. However, a number of key research challenges need to be addressed before robotic systems can be deployed to physically collaborate with human co-workers with varying strengths and in typically unstructured industrial environments. This talk will first discuss challenges of research on physical human-robot collaboration (pHRC), then the development of intelligent robotic coworkers that physically collaborate with humans performing labour intensive tasks such as abrasive blasting and patient handling. Topics include (1) assistance-as-needed paradigm; (2) control methods of robotic co-workers; (3) safety framework for physical human-robot collaboration; (4) brain-robot interface for intuitive human-robot collaboration; (5) modelling of human performance in pHRC; (6) development of robotic co-workers: an Assistance-as- Needed roBot (ANBOT) and a Smart Hoist
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.
Dr. Malika Meghjani is an Assistant Professor in the Computer Science and Design Pillar at Singapore University of Technology and Design (SUTD). She directs the Multi-Agent Robot Vision and Learning (MARVL) Lab, with the focus on algorithm design for efficient, reliable and scalable robots that can work independently and collaboratively with humans. Her research interests are in planning under uncertainty, reinforcement learning, computer vision, deep learning, and game theory. The applications of her work are in field robotics ranging from marine robots specifically, underwater and surface vehicles to self-driving cars and other ground vehicles in unstructured environments. Malika has been cited by Analytics Insight in 2020 as one of the World's 50 Most Renowned Women in Robotics. She is also 2017 SMART Postdoctoral Scholar, 2015 McGill Scarlet Key recipient, 2013 IEEE Canada Women in Engineering Prize awardee and 2013 Google Anita Borg Scholar.
Human-Centric Multi-Robot Systems
In this talk, I will present multi-robot systems in real-world environments that coordinate and collaborate with humans. Specifically, heterogeneous fleet of autonomous mobility-on-demand vehicles that coordinate with each other and human passengers to seamlessly connect the journey from first mile to last mile. A pair of unmanned aerial vehicles that collaborate with human operators for search and rescue operations. Lastly, a heterogeneous group of robots that collaborate with marine biologists for repetitive environment monitoring.
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.
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.
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.
Katja Mombaur (AM’04-M’05) Prof. Dr. Katja Mombaur joined the University Waterloo in March 2020 as Full Professor and Canada Excellence Research Chair (CERC) for Human-Centred Robotics & Machine Intelligence. Her research focuses on understanding human movement by a combined approach of model-based optimization and experiments and using this knowledge to improve motions of humanoid robots and the interactions of humans with exoskeletons, prostheses and external physical devices. Her goal is to endow humanoid and wearable robots with motion intelligence that allow them to operate safely in a complex human world. Prior to coming to Canada, she has been a Full Professor at the Institute of Computer Engineering of Heidelberg University and head of the Optimization, Robotics & Biomechanics group, as well as coordinator of the Heidelberg Center for Motion Research. She holds a diploma degree in Aerospace Engineering from the University of Stuttgart and a PhD In Applied Mathematics from Heidelberg. She has coordinated the European project KoroiBot, has been part of several other European projects such as Spexor, MOBOT and ECHORD, and still is a partner in the ongoing European projects Eurobench and Agilis, and one of the directors of the HeiAge project in Heidelberg.
Model-based optimization for improving the motion intelligence of human-centred robots
Human-centred robots have the potential to support and facilitate people’s lives, ranging from improved well-being and increase independence to reduced risk or harm and a removal of boring jobs. They can take the form of humanoid robots, wearable robots or other types of mobility assistance robots and have to enter in in close physical interactions with humans or support them physically. For this, human-centred robots require motion intelligence or embodied intelligence that makes the robot aware of how it moves in and interacts with its dynamic environment and with humans. In addition to biomechanical studies of human behavior, model-based optimization or optimal control is a widely used approach for generating and controlling motions of human-centered robots. Optimization can tackle the challenges of such systems which include a high complexity, redundancy, underactuation, and a high risk of instability and falls. In this talk, I will present different examples from my research group on using model-based optimal control to control different humanoid robot platform and to improve the design and control of wearable robots for the lower limbs and the lower back and other assistive devices. I will discuss different levels of modeling robots – and in some cases also the interacting humans - to address specific research questions. In addition, I will discuss possible combinations of optimal control methods with reinforcement learning and movement primitive approaches to reduce computation times and improve robot control.
What do we optimize? Inverse Optimal Control as a Tool to Understand Human
Gaining a fundamental understanding of the movements of the human body has long been an important research topic in biomechanics, sports science, physiology, neuroscience, computer animation and many areas of robotics and human-robot interaction. How do humans choose their motions out of the infinite number of ways to perform a given task? And how do motions change based on the situation, or based on the person’s age, training level or medical condition? It is a common assumption that motions of humans and animals – similar to many other processes in nature - are performed in an optimal way due to evolution, learning and training. Optimality principles can be found in the mechanical properties of the executed movements, but also in the closed loop sensory motor system. However, the particular criterion optimized is highly dependent on the specific case and situation and not easy to determine. In this talk, I discuss inverse optimal control as a very promising systematic approach to identify the underlying optimality principles of human movement. Starting from (partial) motion capture data of a specific
human movement or a set of movements of multiple subjects and subject-specific mathematical models, inverse optimal control tells us which objective function – or typically combination of multiple objective functions - gives the closest approximation of the recorded data. I will present algorithmic approaches for solving inverse optimal control as well as a number of examples, including walking on different terrains, running motions of amputees and non-amputees, painting, lifting motions with and without back pain, and interactions with robotic manipulandum. Inverse optimal control has lead to very promising results in these biomechanical studies, and certainly has a huge potential for a much wider
Predictive Processing as a Unified Principle for Cognitive Development
A neuroscientific theory called predictive coding suggests that the human brain works as a predictive machine. That is, the brain tries to minimize prediction errors by updating the internal model and/or by affecting the environment. We have been investigating to what extent the predictive coding theory accounts for human intelligence and whether it provides a unifying principle for the design of robot intelligence. My talk presents computational neural networks we designed to examine how the process of minimizing prediction errors lead to cognitive development in robots. Our experiments demonstrated that both non-social and social cognitive abilities such as goal-directed action, imitation, estimation of others’ intentions, and altruistic behavior emerged as observed in infants. Not only the characteristics of typical development but also those of developmental disorders such as autism spectrum disorder were replicated as a result of aberrant prediction abilities. These results suggest that predictive coding provides a unified computational theory for cognitive development (Nagai, Phil Trans B 2019).
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.
Univ. Prof. Dr. Cristina Olaverri-Monreal is the president of the IEEE Intelligent Transportation Systems Society (IEEE ITSS), founder and chair of the Austrian IEEE ITSS chapter, and chair of the Technical Activities Committee (TAC) on Human Factors in ITS.
She is professor and holder of the BMK endowed chair Sustainable Transport Logistics 4.0 at the Johannes Kepler University Linz, in Austria. Prior to this position, she led diverse teams in the industry and in the academia in the US and in distinct countries in Europe.
She received her PhD from the Ludwig-Maximilians University (LMU) in Munich in cooperation with BMW. Her research aims at studying solutions for an efficient and effective transportation focusing on minimizing the barrier between users and road systems. To this end, she relies on the automation, wireless communication and sensing technologies that pertain to the field of Intelligent Transportation Systems (ITS).
Dr. Olaverri is a member of the EU-wide platform for coordinating open road tests (Cooperative, Connected and Automated Mobility (CCAM)) as well as a representative for the European technology platform "Alliance for Logistics Innovation through Collaboration in Europe" (ALICE) for the "Workgroup Road Safety" (WG4: EU-CCAM-WG-ROAD-SAFETY@ec.europa.eu). She is additionally a senior/associate editor and editorial board member of several journals in the field, including the IEEE ITS Transactions and IEEE ITS Magazine.
Furthermore, she is an expert for the European Commission on "Automated Road Transport" and consultant and project evaluator in the field of ICT and "Connected, Cooperative Autonomous Mobility Systems" for various EU and national agencies as well as organizations in Germany, Sweden, France, Ireland, etc. In 2017, she was the general chair of the "IEEE International Conference on Vehicles Electronics and Safety" (ICVES'2017). She was awarded the "IEEE Educational Activities Board Meritorious Achievement Award in Continuing Education" for her dedicated contribution to continuing education in the field of ITS.
Intelligent automated vehicles and vulnerable road users
Maximum automation in vehicles requires sensing the environment, analyzing information to make the appropriate decisions, and taking the appropriate actions. This process requires overcoming many challenges, including the detection of other road users. The protection of vulnerable road users has been an active research topic in recent years. In this context, P2V (pedestrian-to-vehicle) and V2P (vehicle-to-pedestrian) have become crucial technologies to minimize potential hazards due to the high detection rates and high user satisfaction they achieve.
In this context, the trust of other road users in the technology plays a crucial role when interacting with highly automated vehicles. This presentation will provide an overview of the impact of automated technologies on road safety and identify ways to increase trust in the system.
Effects of conditional and high levels of automation on road safety
The ability to incorporate new technology-enabled functions into vehicles has played a central role in the development of motor vehicles. The widespread application of digital technologies provides the opportunity to design systems whose operation is based on multiple, interconnected applications. As a result, the development of intelligent road vehicle systems such as cooperative driver assistance systems (Co-ADAS), and thus the level of vehicle automation, is rapidly increasing. The advent of vehicle automation is leading to a reduction in driver workload. However, depending on the level of automation, consequences for passengers are foreseeable, such as out-of-the-loop conditions. This presentation will provide an overview of the impact of such technologies on traffic awareness to improve driving performance and reduce traffic accidents. It will also highlight the benefits and potential problems of vehicle automation.
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.
Cường was born in Hanoi, Vietnam. He is an alumnus of École Normale Supérieure, rue d’Ulm (France) and holds a Ph.D. in Neuroscience from Université Pierre et Marie Curie (France). He was a visiting researcher at the University of São Paulo (Brazil) in 2010, and a JSPS Fellow at the University of Tokyo (Japan) in 2011-2013. He joined NTU (Singapore) in 2013 and is currently an Associate Professor in the School of Mechanical and Aerospace Engineering. He was a recipient of the Best Paper Award at the conference Robotics: Science and Systems, 2012. His research has featured in major international media, including The New York Times, The Guardian, The Economist, CNN, Science, Nature, etc. He is also a Founder and Director of Eureka Robotics
(https://eurekarobotics.com/), a deep-tech startup devoted to solving the toughest automation challenges in manufacturing. Eureka Robotics is a recipient of the 2019 IEEE N3XT Star award.
Model-based robotic manipulation with contact and dynamics
Planning and executing motions in the presence of contacts and significant dynamics effects still constitute a major challenge in robotics. Of particular interest, for example, is the computation of dynamically-feasible motions in fractions of a second for real-world industrial applications, such as robotic pick-and-place. We have developed new methods for planning dynamically-feasible motions within milliseconds, based on completely new approaches to solving the Time-Optimal Path Parameterization (TOPP) problem, and to utilizing it as a subroutine in kinodynamic motion planners (Admissible Velocity Propagation). I will discuss our theoretical framework, as well as applications ranging from the "waiter motion" to critically-fast pick-and-place with suction cups. Along the way, I will also present a number of complex tasks involving contacts and dynamics we have tackled in recent years: automatic precision drilling, autonomous assembly of an IKEA chair, large-scale 3Dprinting by a team of mobile robots, etc. These complex tasks illustrate the need for building robust and scalable robotic systems that address multiple challenges, from precise localization, to motion planning, to control of contact forces. Videos of the demos can be found on our channel: https://www.youtube.com/c/CRIGroupRobotics
Taipa, Macau, China
Yan Qiao (M’16-SM’21) received the B.S. and Ph.D. degrees in Industrial Engineering and Mechanical Engineering both from Guangdong University of Technology, Guangzhou, China, in 2009 and 2015, respectively. From Sep. 2014 to Sep. 2015, he was a Visiting Student with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA. From Jan. 2016 to Dec. 2017, he was a Post-Doctoral Research Associate with the Institute of Systems Engineering, Macau University of Science and Technology, Macao. Since Jan. 2018, he is an Assistant Professor at the Institute of Systems Engineering, Macau University of Science and Technology, Macao. He has over 80 publications, including one book chapter and 30+ regular papers in IEEE Transactions. Besides, he was a recipient of QSI Best Application Paper Award Finalist of 2011 IEEE International Conference on Automation Science and Engineering, Best Student Paper Award of 2012 IEEE International Conference on Networking, Sensing and Control, Best Conference Paper Award Finalist of 2016 IEEE International Conference on Automation Science and Engineering, Best Student Paper Award Finalist of 2020 IEEE International Conference on Automation Science and Engineering, 2021 Hsue-shen Tsien Paper award of IEEE/CAA Journal of Automatica Sinica, and Best Paper Award in Application of 2022 IEEE International Conference on Networking, Sensing and Control. He has served as a reviewer for a number of journals. His research interests include scheduling and optimization, semiconductor manufacturing systems, and smart manufacturing.
Real-time control policy for time-constrained single-arm cluster tools with activity time variation
With wafer residency time constraints, to obtain a feasible schedule for cluster tools is to balance the wafer sojourn time to some extent among the processing steps. However, the wafer sojourn time fluctuation caused by activity time variation can result in the violation of residency time constraint at some step such that a feasible schedule under the assumption of deterministic activity times may become infeasible. Thus, it is a great challenge to operate a cluster tool subject to wafer residency time constraints and activity time variation. This topic introduces the real-time operation issues of single-arm cluster tools. In this topic, we show that the system is modeled by a Petri net; with this model, a real-time control policy is proposed to offset the activity variation as much as possible by dynamically adjusting the robot waiting times. In this way, an off-line schedule under deterministic activity times is made to be adaptable to certain activity time disturbance such that it becomes feasible. An illustrative example is given to show the applications of the work.
Scheduling of cluster tools with strict wafer residency time constraints and chamber cleaning requirements
Cluster tools have played a significant role in the entire process of wafer fabrication. As the width of circuits shrinks down to less than 10nm, strict operational constraints are imposed on the operations of cluster tools in order to ensure the quality of processed wafers. Particularly, wafer residency time constraints and chamber cleaning requirements are commonly seen in etching, chemical vapor deposition, etc. They make a scheduling problem of cluster tools more challenging. This topic introduces the scheduling analysis for cluster tools with wafer residency time constraints and chamber cleaning requirements. To solve such a challenging problem, a novel virtual wafer-based scheduling method is proposed. By this method, under a steady state, a process module (PM) processes either a real or virtual wafer at a time. When a PM processes a virtual one, its chamber may perform a cleaning operation. In this way, we can meet not only the strict residency time constraints for real wafers, but also innovatively meet chamber cleaning requirements. Based on such a novel scheduling method, an efficient binary integer programming model is established to optimize the throughput of cluster tools. Experimental results show the efficiency and effectiveness of the proposed method.
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.
Paolo Robuffo Giordano
Primary Areas: Aerial Systems: Mechanics and Control; Aerial Systems: Perception and Autonomy; Calibration and Identification; Multi-Robot Systems; Optimization and Optimal Control; Robust/Adaptive Control of Robotic Systems; Reactive and Sensor-Based Planning; Sensor Fusion; Sensor-based Control; Visual Servoing; Wheeled Robots
Secondary Areas: Compliance and Impedance Control; Localization; Mobile Manipulation; Motion and Path Planning; Nonholonomic Mechanisms and Systems; Nonholonomic Motion Planning; Visual-Based Navigation; Telerobotics and Teleoperation
Start Date- 1 January 2019
End Date- 31 December 2023
Dr Rong SU (M11 – SM14) obtained his Bachelor of Engineering degree from University of Science and Technology of China in 1997, and Master of Applied Science degree and PhD degree from University of Toronto in 2000 and 2004, respectively. He was affiliated with University of Waterloo and Technical University of Eindhoven before he joined Nanyang Technological University in 2010. Dr Su's research interests include multi-agent systems, discrete-event system theory, model-based fault diagnosis, cyber security analysis and synthesis, control and optimization of complex networks with applications in flexible manufacturing, intelligent transportation, human-robot interface, power management and green buildings. In the aforementioned areas he has more than 220 journal and conference publications, 1 monograph, and 6 granted/filed patents. Dr Su is a senior member of IEEE, and an associate editor for Automatica (IFAC), Journal of Discrete Event Dynamic Systems: Theory and Applications, and Journal of Control and Decision. He was the Chair of the Technical Committee on Smart Cities in the IEEE Control Systems Society in 2016-2019. Currently, he is a co-chair of Technical Committee on Automation in Logistics in the IEEE Robotics and Automation Society, and chair of the Control Systems Chapter, Singapore.
Title and abstract of possible talks
Talk 1: About Cyber Security in Discrete-Event Dynamic Systems: from Modelling and Analysis of Smart Attacks to Attack-Resilient Supervisory Control
Abstract: It is a long-lasting dream for human beings to have a fully connected society, where a person can talk to anyone, and monitor and control any process located anywhere at any time. With the continuous advancement of ICT, in particular, the recent 5G-based IoT technologies, this dream is finally getting closer to reality. While we are enjoying this unprecedented connectivity around the world, the threat of cyberattacks that may potentially cause significant damages to human lives and properties has more frequently become the center of attention, which has been attracting lots of research from different communities, including the discrete-event system community, where different attack models are conjectured and analyzed, upon which relevant defense techniques have been proposed.
Considering the extremely broad scope of this subject, in this talk I will narrow my focus to a specific area, that is, cyber security of supervisory control systems. Since the supervisory control theory was proposed in 1980’s in the seminar work by Peter Ramadge and Murray Wonham, it has been applied to many applications that have clear event-driven characteristics, e.g., patient support systems, theme-park recreational systems, water locking systems and most notably, manufacturing systems, in particular, those aiming for manufacturing on-demand in Industry 4.0, which require advanced modelling, analysis, synthesis and execution techniques to cope with dynamic changes in a system's operational environment and production requirements. A closed-loop supervisory control system relies on correct and timely data communication between a plant and its supervisor to ensure correct and performance-optimal behaviors, making it a perfect target of cyberattacks. Considering the diversity of cyberattacks on discrete-event systems, I will focus on a special type of attacks called “smart attacks”, which, if exist, will not be detected by the supervisor until an irreversible process towards ensured damages takes place. An attack may be carried out in either the observation channel, or the command channel, or both simultaneously. By introducing some models of observation and command channel attacks, I will first describe, from an attacker’s point of view, how to synthesize a smart attack strategy. Sufficient and necessary conditions will be given to ensure the existence of such a strategy, which turns out to be decidable, if perfect system knowledge is possessed. In case an attack strategy exists, its synthesis is in general NP-hard. On the other hand, even with prior knowledge of the synthesis procedure of a smart attack, an effective synthesis of an attack-resilient supervisor is still a daunting challenge, owing to not only high synthesis complexities, but also the currently unknown decidability nature of existence of such a supervisor in a general setup. I will show that, for a case where an attacker does not aim for assured damages, but rather some possibility of damages, captured by the concept of weak attackability, the existence of an attack-resilient supervisor is decidable, and a specific synthesis algorithm will be given.
Talk 2: Resilient Resource Management in Multi-Process Manufacturing SystemsAbstract: The 4th industrial revolution is making on-demand manufacturing a reality, which can offer many advantages. However, such anticipated advantages demand for advanced modelling, optimization and real-time implementation techniques to handle highly dynamic user requirements and operational environment. In this talk, we address the problem of resilient resource management for a typical multi-process manufacturing system, which consists of several parallel production processes. Each process has its own resources such as machines, AGVs, conveyors and storages, and production orders from customers with relevant pre-computed production schedules. Due to either changes of production orders or component faults, frequently some process may be interrupted. To ensure continuous operations, the current practice typically relies on a large number of standby components, resulting in very low resource utilization. To ensure fast responsiveness to system changes with low costs, we propose a novel resource sharing strategy that real-time analyzes the resource demand in a target process, and resource flexibilities in other processes, and dynamically assign and schedule flexible resources to the target process to minimize the negative impact of resource scarcity in the target process to its assigned production jobs, while ensuring jobs in other processes not being affected. A general mixed integer linear programming (MILP) formulation is adopted to capture resource needs and flexibility, and a real-time responsive coordinator is designed to ensure fast resource matching and task re-scheduling in relevant processes. The developed strategy is applied to a simulated manufacturing system with multiple fault scenarios, and the experimental results show the promising potential of our strategy in improving job completion rate, increasing resource utilization and achieving load balance.
Daejeon, South Korea
Dr. Jee-Hwan Ryu is an Associate Professor in the Department of Civil and Environmental Engineering at Korea Advanced Institute of Science and Technology (KAIST). He 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 1997 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. He joined KAIST in 2019 as an associate professor. Prior to that, he was a professor in the department of mechanical engineering at KOREATECH (2005-2019), and a research professor in the department of electrical engineering at KAIST (2003-2005). 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 organizations, and especially, he has been served 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.
Cosimo Della Santina
Cosimo Della Santina is Assistant Professor at TU Delft and Research Scientist at the German Aerospace Institute (DLR) since 2020. He received his PhD in robotics (cum laude, 2019) from University of Pisa. He was then a visiting PhD student and a postdoc (2017 to 2019) at the Massachusetts Institute of Technology (MIT), and a postdoc (2020) and guest lecturer (2021) at the Technical University of Munich (TUM). His work has been recognized through awards including the euRobotics Georges Giralt Ph.D. Award (2020), the “Fabrizio Flacco” Young Author Award of the RAS Italian chapter (2019), and the European Embedded Control Institute PhD award (finalist, 2020). Cosimo is currently a member of several EBs (ICRA, IROS, RAL, Frontiers). His main research interests include (i) Modelling for Control and Model Based Control of Soft Robots, (ii) Combining Machine Learning and Model Based Strategies, (iii) Soft Robotic Hands/prostheses.
Talk #1: Combining physical and artificial intelligence in hand-centric grasping and manipulation
The classic approach to grasping and manipulation with rigid robotic hands generally favored object-centric analytical solutions, which - although very elegant and theoretically sound - has not yet produced the desired outcomes in the practice. This talk aims at discussing a different path, which entails moving the focus from the object to the robotic hand. This shift of perspective opens up several challenges which require a full integration of models, materials, machine learning, and bio-inspiration to be tackled effectively. Combining these ingredients, control intelligence can be embedded directly in the hand mechanics. As a result, soft end-effectors can achieve high-level grasping and manipulation performance when operated by humans. However, such a level of dexterity is still unmatched in autonomous grasp execution. Indeed, classic approaches cannot be applied to this kind of hands, which - by their own nature - do not allow fingertips placement with the required precision and relative independence. On the contrary, data driven approaches could be the key to learn from humans how to manage soft hands, towards higher levels of autonomous grasping capabilities.
Hong Kong, China
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.
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
Tomomichi Sugihara is a researcher of Preferred Networks, Inc. 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 had worked at Kyushu University as a guest associate professor from 2007 to 2010, and at Osaka University from 2010 to 2019 as an associate professor. After that, he held the current position. 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.
Toronto (ON), Canada
"Yu Sun is a Professor in the Department of Mechanical and Industrial Engineering, with joint appointments in the Institute of Biomaterials and Biomedical Engineering, the Department of Electrical and Computer Engineering, and the Department of Computer Science at the University of Toronto (UofT). He is a Tier I Canada Research Chair and Director of the UofT Robotics Institute. His Advanced Micro and Nanosystems Laboratory specializes in developing innovative technologies and instruments for manipulating and characterizing cells, molecules, and nanomaterials.
Sun is the Editor-in-Chief of IEEE Trans. Automation Science and Engineering and has served on the editorial boards of IEEE Trans. Robotics, IEEE Trans. Mechatronics, and a few other journals. Among the awards he received were the McLean Award, IEEE Robotics and Automation Society Early Academic Career Award, NSERC Steacie Memorial Fellowship, and NSERC Synergy Award for Innovation. His group has also won over a dozen best paper awards and finalists in journals and at international conferences.
He was elected Fellow of ASME, IEEE, AAAS (American Association for the Advancement of Science), NAI (US National Academy of Inventors), AIMBE (American Institute of Medical and Biological Engineering), CAE (Canadian Academy of Engineering), and RSC (Royal Society of Canada)."
Primary areas: Material/Parts Handling, Micro and nano Scales, Healthcare and Life Sciences.
Secondary areas: Micromanipulation, end effector, force sensing, microassembling, computer vision, automation at micro-nanometer scales, micro devices, MEMS, microfluidics, biomedical engineering.
San Diego (CA), USA
Talk # 1
On Safe Autonomous Driving: Past, Present, and Future
Engineers and scientists engaged in making artificially intelligent systems have successfully resolved many challenging technical problems and have demonstrated the practical viability of autonomous driving on test tracks and carefully selected roads. These are major milestones in engineering and a clear harbinger of a transformative new era of moving goods, supplies, and people from point A to point B. Yet, along with these accomplishments come many new challenges that are not only of a technical nature, but also of a broader social, legal, and even “ethical” nature. Such issues become more urgent and important as collisions and accidents involving self-driving or semi-autonomous vehicles occur more often – injuring and even killing humans in the real world. A key challenge that needs to be addressed is making sure that the artificially engineered automobiles and humans cohabit in a harmonious, safe, and secure manner. For researchers this provides the exciting opportunity to pursue important problems from a broad range of topics in distributed perception, cognition, planning, and control. We will present a “Human Centered” approach for the development of highly automated vehicle technologies. We will also present a brief sampling of contributions in the development of systems and algorithms to perceive situational criticalities, predict intentions of intelligent agents, and plan/execute actions for safe & smooth maneuvers and control transitions. We will highlight major research milestones in the autonomous vehicles area and discuss issues that require deeper, critical examination and careful resolution to assure safe, reliable, and robust operation of these highly complex systems in the real world.
The research interests of Prof. Vogel-Heuser (*1961) are in the area of systems and software engineering as well as in the modeling of distributed and reliable embedded systems. Especially, her research foci are put on the challenges that result from the increasing demand to produce customer-specific products in the plant manufacturing domain. Engineering and education on hybrid process and heterogeneous distributed and intelligent systems using a human centered approach are also included in her research area.
Prof. Vogel-Heuser received her Dipl. Ing. degree in electrical engineering in 1987 and her Dr.-Ing. degree in mechanical engineering in 1990 from the RWTH Aachen, Germany. She acquired industrial experience over ten years, including a position as technical director for the Siempelkamp Group. After various professorship positions in Hagen, Wuppertal, and Kassel, she was appointed to the Chair of Automation and Information Systems at TUM in 2009. Prof. Vogel-Heuser is a Senior Member of the IEEE and a member of the National Academy of Science and Engineering (acatech). She is also a member of various advisory boards, including the advisory board of the VDI/VDE-GMA and the board of trustees of the German Museum. Prof. Vogel-Heuser is editor of the handbook Industrie 4.0 and the author of more than 500 scientific publications.
Talk: Enabling flexible self-adaptiv intralogistic systems by modular Control Software and Multi Agent System
Abstract: The increasing demand for flexibility affects intralogistic systems, more precisely automated material flow systems demanding reconfigurable systems as well as reduced engineering effort. The talk introduces both aspects sharing results from different research projects on the reconfigurability during runtime by using a metamodel based approach that provides the knowledge base for autonomous decision by the agents. Also connectivity of different mechatronic intralogistic modules can be achieved by using such a metamodel based approach. Finally obstacles and challenges for modularity on control level is introduced.
Cybersecurity for Autonomous Systems
Autonomous vehicle technologies have matured to a stage for applications. Autonomy is an integration of multiple technologies, such as sensing, perception, decision and execution. Each application imposes various constraints and limitations to the above technologies and integration. This talk presents our efforts on development of some technologies for a new generation of autonomous environmental service including autonomous navigation for street sweepers, high-fidelity teleoperation and cyber security for autonomous systems. In particular, this talk focuses on the cybersecurity of autonomous systems. We consider cybersecurity for four scenarios including two attacks on prime sensors and two attacks on actuation systems. Our detection frameworks are described and experiments are illustrated to show the effectiveness.
Multi-Robots Collaborative Mapping and Formation Keeping
Kusatsu, Shiga, Japan
Zhongkui Wang is currently an associate professor at the Research Organization of Science and Technology, Ritsumeikan University, Japan. He received his Ph.D. degree in Robotics from Ritsumeikan University, in 2011. He was a research associate in the Department of Robotics of Ritsumeikan University from 2011 to 2012. From 2012 to 2014, he was a postdoctoral fellow at the same University and visited the Swiss Federal Institute of Technology Zurich (ETHZ) as a guest researcher from 2012 to 2013. From 2014 to 2019, he was an assistant professor in the Department of Robotics of Ritsumeikan University. His research interests include soft robotics, robotic hands, grasping and manipulation, biomedical engineering, and tactile sensing. He is member of the editorial board of the Chinese Journal of Mechanical Engineering, topics board of the Actuators, and committee member of several IEEE conferences (ROBIO, RCAR, UR, WRC SARA, ARM, WCICA, ISR, HAVE). His work was awarded best papers at UR2020, RCAR2018, and M2VIP2017.
Talk #1: Robotic Handling of Food and Agricultural Products
Automation in food agricultural industries is not as developed as in the automotive and manufacturing industries. Several challenges hinder the rapid introduction of robots into the food and agricultural industries, for example, the required processing speed, the cost benefit of using robot systems, lack of handling strategies and robotic end-effectors designed to cope with the large variety and variability of food and agricultural products, and lack of sufficient understanding of the product properties as an “engineering” material for handling tasks. In this talk, I will introduce my work on robotic systems, particularly on robotic grippers, for handling various food and agricultural products, and the modeling and measurements of physical properties of food materials.
Pierre-Brice Wieber is a full-time researcher at INRIA Grenoble and has been a visiting researcher at AIST/CNRS Joint Research Lab in Tsukuba. He has advised 14 PhD students and 6 Post-Docs on topics covering modeling, optimization and control of autonomous vehicles, humanoid and legged robots, industrial and collaborative robots. His specific focus of interest is on model-based safety guarantees. He has been serving as Associate Editor for IEEE Transactions on Robotics, Robotics and Automation Letters and conferences such as ICRA and Humanoids.
Talk # 1
A mathematical approach to Isaac Asimov's Three Laws of Robotics
1/ A robot may not injure a human being. 2/ A robot must obey orders except where they conflict with the First Law. 3/ A robot must protect its own existence as long as this does not conflict with the First or Second Law. I propose to discuss how these broad, eight decades old statements can be approached and implemented in today's autonomous vehicles, humanoids, and collaborative robots, introducing a general mathematical approach to provide the corresponding safety guarantees. This will naturally raise the question of models in decision making, and a few ethical issues.
Eiichi Yoshida is Professor of Tokyo University of Science, at Department of Applied Electronics, Faculty of Advanced Engineering. He received M. Eng. and Ph. D degrees from Graduate School of Engineering, the University of Tokyo in 1996. He then joined former Mechanical Engineering Laboratory, later reorganized in 2001 as National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan. He served as Co-Director of AIST-CNRS JRL (Joint Robotics Laboratory) at LAAS-CNRS, Toulouse, France, from 2004 to 2008, and at AIST, Tsukuba, Japan from 2009 to 2021. He was also Deputy Director of Intelligent Systems Research Institute from 2015 to 2018, Industrial Cyber-Physical Systems Research Center, and TICO-AIST Cooperative Research Laboratory for Advanced Logistics in AIST from 2020 to 2021. He was previously invited as visiting professor at Karlsrule Institute of Technology and University of Tsukuba. He is an IEEE Fellow, and member of RSJ, SICE and JSME. He has published more than 200 scientific papers in journals and peer-reviewed international conferences and co-edited some books. He received several awards including Best Paper Award in Advance Robotics Journal, and the honor of Chevalier l’Ordre National du Mérite from French Government. His research interests include robot task and motion planning, human modeling, humanoid robotics, and advanced logistics technology.
Cyber-Physical Human: Humanoid and Digital Actor together for Understanding and Synthesizing Anthropomorphic Motions
Humanoid robots can be used as a "physical twin" of humans to analyze and synthesize human motions, and furthermore behaviors, while those robots themselves are already useful for applications in industries like large-scale assembly. We intend to integrate humanoids and digital actors into "cyber-physical humans" in a complementary manner to understand, predict and synthesize the behavior of anthropomorphic systems in various aspects. Since it is difficult to measure the control output of humans, we may use humanoids to validate the physical interactions with real world, and digital actors to simulate control and interaction strategies using parameterized models like musculo-skeletal systems. Optimization is one of the key techniques to tackle this challenge. A comprehensive framework is introduced for efficient computation of derivatives of various physical quantities essential for optimization purpose, allowing real-time motion retargeting and musculo-skeletal analysis. I introduce some practical applications such as quantitative evaluation of wearable devices and monitoring of human workload. I believe the human model in cyber-physical space will become important for symbiotic robotic system supporting humans naturally and efficiently responding to societal demands. Some future directions such as remote perception and workspace are also discussed.
Planning Whole-Body Humanoid Motions
Motion planning is generally a technique allowing a robot to move from initial to goal configurations without being obstructed by obstacles in a given environment. Following classical methods based on configuration space (C-space) and artificial potential field, randomized sampling-based motion planning has been proposed in 1990's. It drastically broadened its application areas to high- dimensional robots together with rapid growth of computational power, and started being applied to humanoid robots since 2000's. At early stages humanoid motion planning was done in two stages, first a kinematic and geometric path is planned with simplified model, which is then transformed into dynamic motion including walking. The planning technique evolved to whole-body motion planning unifying kinematics and dynamics of full humanoid structure. Those methods have been validated through various humanoid platforms. In addition, contacts with the environments, which should be avoided in usual motion planning, are actively exploited to improve its ability of exporting in cluttered areas or manipulating bulky objects as humans do. Recent research trends on motion planning have been experiencing integration with machine learning, demonstrating promising results. Future possibilities of humanoids for wider application by benefiting from those advances in motion planning and other robotic technologies are discussed
New Brunswick (NJ), USA
Newark (NJ), United States
MengChu Zhou (Fellow of IEEE) joined the Department of Electrical and Computer Engineering, New Jersey Institute of Technology in 1990, and is now a Distinguished Professor. His interests are in intelligent automation, complex systems and networks, Petri nets, Internet of Things, edge/cloud computing, and big data analytics. He has over 900 publications including 12 books, over 600 journal papers including over 500 IEEE Transactions papers, 30 patents and 29 book-chapters. He is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems, Man, and Cybernetics Society, Distinguished Service Award from IEEE Robotics and Automation Society, and Edison Patent Award from the Research & Development Council of New Jersey. He is Fellow of International Federation of Automatic Control, American Association for the Advancement of Science, Chinese Association of Automation and National Academy of Inventors.
Talk # 1 for TC Digital Manufacturing and Human Centered Automation
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 TC Digital Manufacturing and Human Centered Automation
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.
Talk #1 for TC Semiconductor Manufacturing Automation
Transforming Manufacturing Industry from Automation to Intelligenization with Industry 4.0 Technologies
Industry 4.0 intends to address a fast-changing and challenging manufacturing environment with diverse demands, short order leadtime and product life cycle, limited capacities, and highly complex process technologies. A manufacturing system integrated with Industry 4.0 technologies, such as AI, machine learning, big data analytics, digital twin, and Internet of Things, is capable of performing real-time monitoring and optimization of manufacturing processes in various aspects from high level strategic resource and production planning down to real-time equipment-level smart dispatching and predictive maintenance. By fully using real-time data and AI, the system is able to help manufacturers shorten production and R&D processes, increase production capacity, reduce production cost, guarantee product quality, and improve product yield. It is suitable to help not only high-tech industries such as semiconductor wafer fabrication, but also conventional labor-intensive sectors. This talk illustrates the transformation of semiconductor manufacturing activities from automation to intelligenization by using Industry 4.0 technologies through real-life wafer fabrication applications.
Talk #2 for TC Semiconductor Manufacturing Automation
Modeling, Scheduling and Real-time Control of Cluster Tools in Semiconductor Manufacturing
This talk intends to present Petri nets as a modeling, analysis, optimal scheduling and real-time control tool for single and multicluster 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.
Angel P. del Pobil
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.
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