Speakers 2024

Keynote Speaker Ⅰ

Prof. Yang Cong

South China University of Technology, China

Brief Introduction: Yang Cong is a full professor of Chinese Academy of Sciences. He received the B.S. degree from Northeast University in 2004, and the Ph.D. degree from State Key Laboratory of Robotics, Chinese Academy of Sciences in 2009. He was a Research Fellow of National University of Singapore (NUS) and Nanyang Technological University (NTU) from 2009 to 2011, respectively; and a visiting scholar of University of Rochester. His current research interests include robot vision, robot learning, big data, multimedia and medical image analysis. He won the National Science Fund (NSFC) for both Distinguished Young Scholars and Excellent Young Scholars, the first prize of Natural Science Award of Liaoning Province, the first prize of Natural Science Award of Chinese Association of Automation.  He has published more than 80 papers in the international journals and conferences. He served as the associated editor of IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Journal of Automation, and other well-known journals.

Speech Title: Robot Embodied Artificial Intelligence and Manipulation Skill Generalization

Abstract: The ability of perception and cognition is important for intelligent robots, where Embodied Artificial Intelligence are essential strategies for robot perception and cognition. Although many prominent progresses have been achieved in recent years, for example the accuracy of object detection and vision-language application are continuously improved on ChatGPT and other big models, some of the core problems have not been well handled, where the robot still cannot accomplish many seemingly simple work as human ourselves, e.g., folding clothes. Generally, two issues need to be concerned carefully, i.e., the ability of robotic generalization manipulation and the ability of autonomous human-like continue learning. In this talk, we will discuss some new technologies, new problem, applications, and developmental tendencies.

Keynote Speaker Ⅱ

Prof. Xiaolong Zheng

Institute of Automation, Chinese Academy of Sciences, China

Brief Introduction: Xiaolong Zheng is currently a Research Professor at the Institute of Automation, Chinese Academy of Sciences (CASIA), and Professor at the School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS). He received Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, in 2009, M.S. degree from Beijing Jiaotong University in 2006, and B.S. degree from China Jiliang University in 2003. Xiaolong Zheng has published more than 150 peer-to-peer academic papers in journals/magazines and conferences. His research interests including big data analytics and social computing, decision intelligence, human-machine cognitive alignment and general artificial intelligence. Xiaolong Zheng has served as the Program Co-chairs of more than 10 international Conferences, the Technical Program Committee Member for more than 50 international conferences, and the associate editor of several reputable journals.

Speech Title: Detecting Social Bots on Social Media Platforms Using the Compatibility-aware Graph Neural Network

Abstract: With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a Compatibility-aware Graph Neural Network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks.

Keynote Speaker Ⅲ

Prof. Yongping Pan

Sun Yat-sen University, China

Brief Introduction: Yongping Pan is a Professor who leads the Intelligent Robotics Lab at the Sun Yat-sen University, Shenzhen, China. He holds a Ph.D. degree in control theory and control engineering from the South China University of Technology, Guangzhou, China, and has over ten years of research experience in top universities in Singapore and Japan. His research interests lie in automatic control and machine learning for robotics. He has authored or co-authored more than 180 peer-reviewed academic papers, with over 130 papers in refereed journals. His publications have attracted over 7400 and 5700 citations in the Google Scholar and Web of Science Core Collection, respectively. Dr. Pan is currently serving as the Chair of the IEEE Robotics and Automation Society Guangzhou Chapter and an Associate Editor of six top-tier journals published by IEEE and IFAC. He has served as an Organizing Committee Member of five international conferences and the Lead Workshop Organizer of the IEEE Conference on Decision and Control. He has been recognized as a Global Highly Cited Researcher by Clarivate, a Most Cited Chinese Researcher by Elsevier, and a World Top 2% Scientist (both single year and career) by Stanford University. Furthermore, he has been invited to deliver academic talks at leading universities and conferences over 60 times worldwide.

Speech Title: Composite Learning Tracking and Interaction Control for Compliant Robots

Abstract: With the rapid population aging globally, the current trend of robotic research has been shifting from traditional industrial robots to human-centered robots that coexist, cooperate, or collaborate with humans, including service robots. Due to the existence of physical human-robot interaction, we usually introduce compliance to human-centered robots. This talk introduces our major results in tracking and interaction control for robots driven by compliant actuators. First, we establish a data-driven online learning methodology termed composte learning inspired by the cerebellum learning and control mechanism, and its rigorous theoretical results on consistent learning and strong robustness, which revolutionizes existing adaptive systems that are difficult to learn online and vulnerable. Then, we solve a series of key theoretical challenges in the robotic applications of composite learning, and apply it to trajectory tracking and interaction control of robots driven by compliant actuators, which improves their overall accuracy, safety, and naturalness.

Keynote Speaker Ⅳ

Assoc. Prof. Chen Lv

Nanyang Technological University, Singapore

Brief Introduction: Chen Lv is an Associate Professor at School of Mechanical and Aerospace Engineering, and the Cluster Director in Future Mobility Solutions, Nanyang Technological University, Singapore. He received his PhD degree at Department of Automotive Engineering, Tsinghua University in 2016, with a joint PhD at EECS Department, University of California, Berkeley. He was a Research Fellow at Cranfield University, UK during 2016-2018. He joined NTU as a Nanyang Assistant Professor and founded the Automated Driving and Human-Machine System (AutoMan) Research Lab in June 2018, and got promoted to Associate Professor with Tenure in August 2023. His research focuses on AI, robotics, automated driving, and human-machine systems, where he has published 4 books, over 100 papers, and obtained 12 granted patents. He serves as Associate Editor for IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Vehicular Technology, IEEE Transactions on Intelligent Vehicles, etc. He received many awards and honors, selectively including IEEE IV Best Workshop/Special Session Paper Award (2018), Automotive Innovation Best Paper Award (2020), Winner of Waymo Open Dataset Challenges at CVPR (2021, 2022), Winner of IEEE VTS Motor Vehicles Challenge (2022), Machines Young Investigator Award (2021), Nanyang Research Award (Young Investigator) (2022), Most Innovative Award of NeurIPS Driving SMARTS Competition (2022), SAE Ralph R. Teetor Educational Award (2023), CVPR nuPlan Planning Challenge Innovation Prize (2023), and IEEE ITSC 2023Best Paper Runner-Up Award.

Speech Title: Human-like Autonomy for Smart Mobility and Robotics

Abstract: The long-term goal of artificial intelligence (AI) systems is to make them learn, think and act smartly like human beings. As a typical application of AI, autonomous vehicles (AVs) become one of the most potential and ultimate ambitions in the smart mobilities. They primarily designed to replace human drivers during driving in order to enhance the performance and avoid the possible fatalities. In the near future, AVs are believed to share public roads with human-driven vehicles, which requires AVs to be smart and able to behave like human drivers, being reasonable and predictable to other road users. However, due to their limited smartness, current AVs are still lack of robust situation understanding, interaction prediction and human-like decision-making abilities when interacting with others, particularly in complex and emergency situations. Therefore, human-machine hybrid intelligence, as well as human-machine collaboration, are of great importance to ensure the safety and further improve the smartness of mobility systems, during long-term development and large-scale deployment of AVs. In this talk, the recent studies in human-like autonomy and human-machine hybrid intelligence for future mobility will be presented. First, a data-driven prediction and decision-making framework for human-like autonomous driving will be introduced. Next, a novel human-machine collaboration framework with bi-directional performance augmentation ability developed for automated vehicles and robotics will be presented in detail.