ICARM 2023
Plenaries/Keynotes
| NAME | TITLE | TIME |
| Lining Sun | Frontier Robotics Technology and Cross Innovation Development | Saturday July 8, 09:40 – 10:20 |
| Danica Kragic | Representation Learning and Evaluation for Interaction Tasks | Saturday July 8, 10:40 – 11:20 |
| Yang Tang | Perception and Decision-Making in Autonomous Intelligent Systems | Saturday July 8, 11:20 – 12:00 |
| Xinyu Liu | Soft Sensors, Electronics and Robots | Sunday July 9, 09:00 – 09:40 |
| Dongrui Wu | Machine Learning for Brain-Computer Interfaces | Sunday July 9, 09:40 – 10:20 |
| Wei Ren | Distributed Control, Estimation and Optimization in Multi-agent Systems | Sunday July 9, 10:40 – 11:20 |
| Yajing Shen | Bioinspired Small Robotics for Biomedical Engineering | Sunday July 9, 11:20 – 12:00 |
Lining Sun, Professor, Soochow University
Frontier Robotics Technology and Cross Innovation Development
Abstract: Currently, a large number of intelligent equipment represented by robots have emerged, becoming an important support for the digital economy and advanced manufacturing industry. The empowerment of new technologies such as artificial intelligence, industrial internet and big data has been accelerating the innovative development of robot technology. Based on this development status, this talk will share cutting-edge technologies such as bionic robots, micro robots, bio-electromechanical integration, intelligent perception and human-robot interaction, as well as the development trend of digitalization, networking and intelligence under the deep integration of multiple technologies. At the same time, it will also put forward targeted development suggestions around the needs of intelligent transformation and digital transformation of China’s manufacturing industry.
Biography: Prof. Lining Sun received the Ph.D. degree in engineering from the Mechanical Engineering Department, Harbin Institute of Technology (HIT), Harbin, China, in 1993 , and joined HIT after graduation. Prof. Sun has been engaged in the frontier research of robotics for more than 30 years and he formed an internationally competitive robotics team and made outstanding contributions to the creation and development of robot-related disciplines in China. Prof. Sun is a Russian Academy of Engineering (RAE) Foreign Full Member, a National Outstanding Youth Fund Winner and a Changjiang Scholar Distinguished Professor by the Ministry of Education.
Danica Kragic, Professor, Royal Institute of Technology – KTH
Representation Learning and Evaluation for Interaction Tasks
Abstract: All day long, our fingers touch, grasp and move objects in various media such as air, water, oil. We do this almost effortlessly – it feels like we do not spend time planning and reflecting over what our hands and fingers do or how the continuous integration of various sensory modalities such as vision, touch, proprioception, hearing help us to outperform any other biological system in the variety of the interaction tasks that we can execute. Largely overlooked, and perhaps most fascinating is the ease with which we perform these interactions resulting in a belief that these are also easy to accomplish in artificial systems such as robots. When interacting with objects, the robot needs to consider various objects’ properties. The focus in our work is on physical interaction with deformable objects using multimodal feedback, generative models and address stability in contact rich tasks. In this talk, we will focus on how to create new informative and compact representations of deformable objects that incorporate both analytical and learning-based approaches.
Biography: Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. She chaired IEEE RAS Technical Committee on Computer and Robot Vision and served as an IEEE RAS AdCom member. Her research is in the area of robotics, computer vision and machine learning. In 2012, she received an ERC Starting Grant. Her research is supported by the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Swedish Research Council. She is an IEEE Fellow.
Yang Tang, Professor, East China University of Science and Technology
Perception and Decision-Making in Autonomous Intelligent Systems
Abstract: In this talk, we will review our recent advances in perception and decision-making in autonomous intelligent systems. We will first report our results in unsupervised depth estimation via deep learning in dynamic environment. Then, we will show our results adapted to different extreme conditions like night, rainy night and snow days. After giving our results in perception of complex environment, we will also present our results in decision-making and coordination control of UAV. Finally, some concluding remarks will be provided.
Biography: Yang Tang received the B.S. and Ph.D. degrees in electrical engineering from Donghua University, Shanghai, China, in 2006 and 2010, respectively. From 2008 to 2010, he was a Research Associate with The Hong Kong Polytechnic University, Hong Kong. From 2011 to 2015, he was a Post-Doctoral Researcher with the Humboldt University of Berlin, Berlin, Germany, and with the Potsdam Institute for Climate Impact Research, Potsdam, Germany. He is now a Professor with the East China University of Science and Technology, Shanghai. His current research interests include distributed estimation/control/optimization, cyber-physical systems, hybrid dynamical systems, computer vision, reinforcement learning and their applications.
Prof. Tang was a recipient of the Alexander von Humboldt Fellowship and has been the ISI Highly Cited Researchers Award by Clarivate Analytics from 2017. He is a Senior Board Member of Scientific Reports, an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Circuits and Systems-I: Regular Papers, IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Systems Journal and Engineering Applications of Artificial Intelligence (IFAC Journal), etc. He is a (leading) guest editor for special issues in four IEEE Transactions.
Xinyu Liu, Professor, University of Toronto
Soft Sensors, Electronics and Robots
Abstract: In the past decades, significant efforts have been spent on developing soft sensors, electronics, and robots for more efficient and seamless interactions between human and engineering systems. In addition, natural biological systems have also provided enormous inspirations for novel biomimetic designs of soft robots. Together, all these activities have gradually blurred the boundary between our natural biological systems and artificial machines, which may eventually realize the scientific fantasy of cybernetic organisms (cyborgs). My research group at University of Toronto is working at the interface of soft/stretchable sensors, electronics, and robotics. In this talk, I will describe our recent work on developing polymer-based soft sensors and electronics and introduce our progress on optogenetic locomotion control of a living soil-dwelling nematode C. elegans for microrobotic applications. Experimental results and demonstrations will be presented to showcase how new soft materials and structures are designed to construct multifunctional sensors and electronics for wearable sensing, human-machine interaction, and soft robotics; and how the optogenetic worm locomotion control strategy could potentially convert a small organism into a living soft microrobot. Finally, the future work along these directions will be briefly discussed.
Biography: Xinyu Liu is the Percy Edward Hart Professor in the Department of Mechanical and Industrial Engineering, University of Toronto (U of T). Prior to joining U of T, he was an Associate Professor and the Canada Research Chair in Microfluidics and BioMEMS in the Department of Mechanical Engineering at McGill University. He obtained his B.Eng. and M.Eng. from Harbin Institute of Technology in 2002 and 2004, respectively, and his Ph.D. from the University of Toronto in 2009, all in Mechanical Engineering. He then completed an NSERC Postdoctoral Fellowship in the Department of Chemistry and Chemical Biology at Harvard University in 2009–2011. At U of T, his research activities primarily focus micro/nanorobotics and microfluidics, with applications in medicine and biology. He received numerous international and national research awards, including 8 best paper awards at major engineering and biomedical conferences. He serves as the Corresponding Chair of the IEEE Robotics and Automation Society Technical Committee for Micro and Nano Robotics and Automation, a Senior Editor of IEEE Robotics & Automation Letters, a Specialty Chief Editor of Frontiers in Robotics and AI, an Associate Editor of IEEE Trans. on Automation Science and Engineering, IEEE Trans. Nanotechnology, and IET Cyber-Systems and Robotics, and a Guest Editor of Engineering (the flagship journal of Chinese Academy of Engineering). He is a Fellow of the Canadian Academy of Engineering, an elected Member of the European Academy of Sciences and Arts, and a Fellow of ASME and CSME.
Dongrui Wu, Professor, Huazhong University of Science and Technology
Machine Learning for Brain-Computer Interfaces
Abstract: A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Thus, sophisticated machine learning approaches are needed for accurate and reliable EEG-based BCIs. This talk will introduce the basic concepts of BCIs, review the latest progress, and describe several newly proposed machine learning approaches for BCIs.
Biography: Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
Prof. Wu’s research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (10000+ Google Scholar citations; h=54). He received the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation Early Career Award in 2021, and the Ministry of Education Young Scientist Award in 2022. His team won the First Prize of the China Brain-Computer Interface Competition in four successive years (2019-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.
Wei Ren, Professor, University of California: Riverside
Distributed Control, Estimation and Optimization in Multi-agent Systems
Abstract: While autonomous systems that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous systems operating in a coordinated fashion. Potential applications for networked multiple autonomous systems include environmental monitoring, search and rescue, space-based interferometers, and hazardous material handling. Networked multi-agent systems place high demands on features such as low cost, high adaptivity and scalability, increased flexibility, great robustness, and easy maintenance. To meet these demands, the current trend is to design distributed algorithms that rely on only local information and local interaction to achieve global group behavior.
The purpose of this talk is to overview our research in distributed control, estimation and optimization in networked multi-agent systems. For distributed control, results on distributed synchronization for agents with various dynamics, distributed single-leader collective tracking with reduced interaction and partial measurements, and distributed multi-leader containment control with local interaction will be introduced. For distributed estimation, results on fully distributed information fusion with multiple networked sensors will be introduced, under very mild assumptions on local observability, communication graphs, and models. For distributed optimization, results on distributed convex optimization will be introduced, under realistic challenges such as non-identical constraints, fully distributed design, and time-varying cost functions. Application examples in multi-vehicle cooperative control will also be introduced.
Biography: Wei Ren is currently a Professor with the Department of Electrical and Computer Engineering, University of California, Riverside. He received the Ph.D. degree in Electrical Engineering from Brigham Young University, Provo, UT, in 2004. His research focuses on distributed control of multi-agent systems. Dr. Ren was a recipient of the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize in 2017 and the National Science Foundation CAREER Award in 2008. He is an IEEE Fellow and an IEEE Control Systems Society Distinguished Lecturer.
Yajing Shen, Professor, Hong Kong University of Science and Technology
Bioinspired Small Robotics for Biomedical Engineering
Abstract: Micro/nano robots have attracted extensive interest in biomedical engineering owing to their great potential to work inside the body for diagnosis, drug delivery, minimally invasive surgery, and so on. In this talk, I’d like to share my ideas on the design and application of the bioinspired small robot for biomedical applications. This talk will start with a brief review of the development of micro/nano robotics followed by the trade-off and challenges to apply them in biomedical engineering. Then, I will introduce some potential solutions/efforts to address these existing challenges by giving some examples in our Lab, including the swimming microrobot, walking robot, and so on. I’d also like to share my own perspective on robotics and intelligence in biomedical engineering and discuss them with the audience.
Biography: Dr. Yajing Shen received the Ph.D. degree in 2012 and he is currently working as an Associate Professor in the Dept. of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). He is also the director of the research center for smart manufacturing and a core member of the robotics institute at HKUST. He is a Senior Member of IEEE, an Executive member of China Micro-nano Robotic Society, and the Associate Editor of IEEE Trans on Robotics (2019-22). Dr. Yajing’s main research interest is small/bioinspired robotics, intelligent systems, and their applications in biomedical engineering. He has published ~100 peer-reviewed journals/conferences, including the top multidisciplinary journal (e.g., Science Robotics, Nature Communications, PNAS), top specialized journal (e.g., IEEE Trans on Robotics), top international conference (e.g., ICRA, IROS), with widely reported by international media, e.g., Associated Press, Thomson Reuters, etc. Dr. Yajing has received serval academic 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, and the Big-on-Small Award at MARSS 2018. He also received the “National Excellent Young Scientist Fund (Hong Kong & Macau)” for the topic “micro/nano robot” in 2019.