Plenary Speech I: Robot Skill Learning in Virtual-Real Scene and 3C Applications

Abstract:

The robot AI is dominated by physical interaction in a closed-loop form. It not only emphasizes the perception and processing of simulated human brain information, but also emphasizes brain-body cooperation to solve the dynamic, interactive and adaptive problems of behavior learning in the dynamic scene. As the core of robot AI, skill learning for robot manipulations is a difficult and important issue in current research. In view of the problems that existing skill learning methods do not make use of the demonstration samples efficiently and cannot achieve efficient policy learning, and the imitation learning algorithm is sensitive to the teaching preference characteristics and the local manipulation space, this talk introduces the skill learning for robot manipulations in virtual-real scenes carrying out in our research team. By using digital twin technology, a virtual scene for robot manipulation is established where new tactile modeling, imitation learning and transfer learning approaches are proposed for skill learning. Furthermore, some enhancement approaches are also developed for skill’s robustness and generalization ability in 3C applications. Finally, the future development of robot skill learning is prospected.

Speaker: Prof. Fuchun Sun

Dr. Fuchun Sun is a full professor of Department of Computer Science and Technology and President of Academic Committee of the Department, Tsinghua University, deputy director of State Key Lab. of Intelligent Technology & Systems, Beijing, China. He also serves as Vice president of China Artificial Intelligence Society and executive director of China Automation Society. His research interests include robotic perception and intelligent control. He has won the Champion of Autonoumous Grasp Challenges in IROS2016 and IROS 2019.    Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 by MOE of China and the Choon-Gang Academic Award by Korea in 2003, and was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He served as an associated editor of IEEE Trans. on Neural Networks during 2006-2010, IEEE Trans. On Fuzzy Systems during 2011-2018, IEEE Trans. on Cognitive and Developement since 2018 and IEEE Trans. on Systems, Man and Cybernetics: Systems since 2015.

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Plenary Speech II: Distributed Machine Learning for Big Models

Abstract:

Machine/Deep learning (ML/DL) systems are important foundations for artificial intelligence and have attracted a lot of attention in academia and industry in recent years. The increasing scale of Deep Learning models and data brings severe challenges to existing systems, and distributed deep learning systems are becoming more and more important. As the intersection of ML/DL and systems, it is necessary to pay attention not only to the data characteristics, model structures, training methods, and optimization algorithms, but also to the execution problems in the computing, storage, communication, scheduling, and hardware of the system. In this talk, I will introduce the current development of "big models" and then share our efforts on the system optimizations for distributed training of big models, as well as the explorations of automated parallel training. Based on these efforts, I will also briefly present our open-sourced system -- Hetu, a new distributed deep learning system for large-scale model training.

Speaker: Prof. Bin Cui

Bin Cui is a professor and Vice Dean in School of CS at Peking University. His research interests include database system, big data management and analytics, and ML system. He has regularly served in the Technical Program Committee of various international conferences including SIGMOD, VLDB and KDD, and is the Editor-in-Chief of Data Science and Engineering, also in the Editorial Board of Distributed and Parallel Databases, Journal of Computer Science and Technology, and SCIENCE CHINA Information Sciences, and was an associate editor of IEEE TKDE and VLDB Journal, and Trustee Board Member of VLDB Endowment. He is serving as Vice Chair of Technical Committee on Database (CCF). He was awarded Microsoft Young Professorship award (MSRA 2008), CCF Young Scientist award (2009), Second Prize of Natural Science Award of MOE China (2014), and appointed as Cheung Kong distinguished Professor by MOE China in 2016.

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Plenary Speech III: Multimodal BCIs and Their Clinical Applications

Abstract:

Despite rapid advances in the study of brain-computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multi-dimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals or multisensory stimuli. Furthermore, multi-dimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. Furthermore, we report several initial clinical applications of these multimodal BCI systems in two patient populations, i.e., patients with disorder of consciousness (DOC) and those with spinal cord injuries (SCIs). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brain mechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.

Speaker: Prof. Yuanqing Li

Yuanqing Li received the B.S. degree in applied mathematics from Wuhan University, Wuhan, China, in 1988, the M.S. degree in applied mathematics from South China Normal University, Guangzhou, China, in 1994, and the Ph.D. degree in control theory and applications from the South China University of Technology, Guangzhou, in 1997. Since 1997, he has been with the South China University of Technology, where he became a Full Professor in 2004. From 2002 to 2004, he was with the Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Japan, as a Researcher. From 2004 to 2008, he was with the Laboratory for Neural Signal Processing, Institute for Infocomm Research, Singapore, as a Research Scientist. He was elevated to IEEE Fellow for his contributions to brain signal analysis and BCIs, 2016. He won State Natural Science Awards (second prize), China, 2009, Changjiang Professorship, Ministry of Education, China, 2012, Distinguished Young Scholar Award, National Natural Science Foundation of China (NSFC), 2008, and so on. He was elevated you to IEEE Fellow for contributions to brain signal analysis and brain computer interfaces, 2016. His research interests include blind signal processing, sparse representation, machine learning, brain–computer interface, EEG, and fMRI data analysis. He has published more than 100 papers in high level journals including Brain, Cerebral Cortex, NeuroImage, Human Brain Mapping, Journal of Neural Engineering, Neural Computation, Proceedings of the IEEE, IEEE Signal Processing Magazine, IEEE Trans. BME, IEEE Trans. IT, and EEE Trans. PAMI. He also has more than 30 publications in conferences including NIPS and WCCI. He has been serving as AE of several journals such as IEEE Trans. on Fuzzy Systems and IEEE Trans. on Human-Machine Systems.