Keynote Speaker 2026
Keynote Speaker I
Prof. Yongchun Xie
Beijing Institute of Control Engineering, China
Brief Introduction: Prof. Dr. Xie is director of Science & Technology Committee and chief technologist in RVD field at Beijing Institute of Control Engineering, senior chief professor of China Academy of Space Technology, director of Tianjin Key Laboratory on Microgravity & Low-gravity Environment Simulation Technology. Her academic positions include a vice chair of IFAC TC on Aerospace, corresponding member of International Academy of Astronautics, fellow and executive council member of Chinese Association of Automation, and editor-in-chief of Aerospace Control and Application.
She has pioneered long-term research in spacecraft intelligent autonomous control theories, established and lead China's space RVD control innovation team, systematically achieved breakthroughs spanning automated, manual and fully autonomous fast RVD. She innovatively proposed Multi-Mode Autonomous RVD Precise Control Theory, constructing a holistic technical architecture that was successfully implemented in 40 RVD missions — including China’s Shenzhou manned spacecraft, Tianzhou cargo vehicles and Chang’e lunar explorers — with the 2-hour fully autonomous ultra-fast RVD creating a new world record in TZ-5 mission. She was awarded 1 Second Prize of China’s National Technological Invention Award, 7 First Prizes of Provincial/Ministerial-Level Science and Technology. She published 5 monographs, 1 technical compendium, 180 high-impact papers, and obtained 90 authorized invention patents. She was awarded the 4th National Innovation Competition Award, the National March 8th Red Banner Pacesetter title, the Outstanding Science and Technology Leading Talent of Central Enterprises award, the Outstanding Contributor to China's Manned Space Program award, the Outstanding Contribution Award of CASC, the First Prize of Yang Jiachi Science and Technology Award, and the WIPO Best Women Inventors Award, and granted China’s Special Government Allowances of the State Council.
Speech Title: Advances in Intelligent Control Technology for Autonomous Perception and Operation of Deep Space Exploration Spacecraft
Abstract: As human space exploration activities advance toward more distant and more complex celestial targets, mission scenarios have gradually expanded from early flyby and orbital observation to new modes including precise soft landing, long-duration planetary surface roving, autonomous sample return, and even future crewed exploration. This report focuses on the intelligent control technologies for deep space exploration spacecraft. First, it reviews the implementation status and future plans of typical deep space exploration missions within the solar system, and summarizes the challenging nature of deep space exploration missions. Then, it systematically elaborates on the development and current research status of artificial intelligence and advanced control methods in areas such as environmental perception, decision-making and planning, fine manipulation, and multi-body coordination, and analyzes the application characteristics and development trends of related technologies. In combination with China's future development needs, it studies intelligent perception and multimodal information fusion methods for fine space manipulation, as well as planning and control methods for dexterous robot manipulation oriented toward multiple types of skills, and verifies the effectiveness of the new methods through ground experiments in application scenarios such as electric drill tool operation, on-orbit refueling, and module replacement. This report can provide a reference for the technical development roadmap of major engineering projects such as future crewed lunar landing, deep space exploration, and on-orbit servicing.
Keynote Speaker II
Prof. Tao Zhou
North Minzu University, China
Brief Introduction: Second-level Professor, Doctoral Supervisor, Visiting scholar at the University of Leicester in the UK and The Chinese University of Hong Kong; Stanford's top 2% global Scientists, ScholarGPS 'Global Top Scholars, and CNKI's Highly Cited Researchers of the Year; He was awarded as an expert of Outstanding Contribution to the National Ethnic Commission, a Leading Talent of the National Ethnic Commission, and a Leading Talent of Science and Technology in Ningxia. Currently, he is the head of the National Ethnic Affairs Commission Key Laboratory of Image and Graphics Intelligent Processing, the leader of the National Ethnic Affairs Commission Medical Information Intelligent Perception and Advanced Computing Innovation Team, and a member of the School Academic Committee; He concurrently serves as a council member of the Chinese Society of Image and Graphics, a council member of the Chinese Society of Stereology, a field chair of the IEEE ICASSP International Conference, a resident reviewer for over 150 international academic journals including TPAMI/CVPR, a communication review expert for the National Natural Science Foundation of China, and a review expert for key research and development projects of the Ministry of Science and Technology. Currently, he is mainly engaged in research in medical AI, computer-aided diagnosis, and medical image processing. He has led more than 30 research projects, including 5 National Natural Science Foundation projects. He led the team to win the Second Prize of the Ningxia Autonomous Region Science and Technology Progress Award and the Third Prize. He has published nearly 270 papers in high-level journals and conferences such as IEEE TPAMI, TCSVT, INFO., PR, CVPR, etc., among which 4 papers are ESI highly cited papers and 1 paper is an ESI hot paper.
Speech Title: Research on Instance Segmentation Method for MRI Images of Head and Neck Lymph Nodes
Abstract: Head and neck malignancies are common types of cancers in terms of incidence and mortality worldwide. Lymph node metastasis in the head and neck region is a typical pathological feature, which determines the recurrence risk and survival prognosis of patients. Medical image instance segmentation is an artificial intelligence task that combines object detection with pixel-level segmentation. It can locate every lesion in the medical image, distinguish different individual lesions of the same type of disease, and output independent and precise contour masks for each lesion. Currently, instance segmentation has become a research hotspot in the field of medical image processing using artificial intelligence technology. This report provides a comprehensive summary of single-stage instance segmentation, presents the 1+2+3 technical framework of single-stage instance segmentation; and reports our team's MambaYOLACT - a head and neck lymph node instance segmentation model for MRI images of head and neck lymph nodes. In response to the issues of candidate box redundancy, insufficient coupling between masks and spatial positions, and insufficient perception of irregular target structures in the Anchor box mechanism in head and neck lymph node MRI images, a heat map-guided SMGF²-YOLACT head and neck lymph node instance segmentation model is proposed.
Keynote Speaker III
Prof. Chengcai Wang
Beihang University, China
Brief Introduction: Professor Wang Chengcai is a doctoral supervisor and a recipient of a prestigious national-level talent award. His research focuses on robot design, intelligent perception, and control theory, with a particular emphasis on developing robotic systems for engineering applications in extreme environments, including deep-sea and deep-space exploration.
He has led more than 10 research projects funded by national and regional programs, including the National Natural Science Foundation of China, key research funding programs, the Beijing Natural Science Foundation, and provincial key R&D initiatives. He holds 30 granted invention patents and has published over 40 academic papers. His honors and awards include the First Prize of a Technology Invention Award, the First Prize of the Science and Technology Award of the Chinese Association for Simulation, the First Prize of the Technology Invention Award of the Chinese Association of Command and Control, and the Beijing Science and Technology Rising Star Award. He also serves as a member of the Aircraft Control Committee of the Chinese Association of Automation and Deputy Secretary-General of the Youth Working Committee of the Chinese Association of Command and Control.
Speech Title: Research on the Design and Cooperative Control of Ground-Air Cross-Domain Robots
Abstract: Ground-air multimodal robots have demonstrated significant application potential in disaster rescue, special inspections, and extraterrestrial exploration. This report reviews recent domestic and international research on ground-air multimodal robots, focusing on the configuration characteristics, actuation methods, and locomotion mechanisms of different robot types. It provides an in-depth analysis of key technologies, including obstacle perception and stable locomotion over complex terrains, autonomous and stable takeoff in unstructured environments, stable flight and trajectory tracking under airflow disturbances, and buffered landing to mitigate ground effects and touchdown impacts. Finally, the report outlines the challenges and future trends in the autonomous and intelligent locomotion of ground-air multimodal robots.
Keynote Speaker IV
Prof. Gang Chen
Beijing University of Posts and Telecommunications, China
Brief Introduction: Gang Chen, professor, doctoral supervisor, a recipient of the National Young Talent Program, and Dean of the School of Intelligent Engineering and Automation at Beijing University of Posts and Telecommunications. His research focuses on intelligent robotics and intelligent control technologies, and he has participated in the development of robotic arms for several flagship national projects—including the core-module robotic arm for China Space Station and the lunar-surface sampling arms for the Chang’e-5 and Chang’e-6 probes—making outstanding contributions to China Space Station program and to China Lunar Exploration Program. He has led over 60 research projects—including NSFC-funded projects, National Key R&D Program projects, and projects of aerospace institute, and has published more than 100 academic papers and four academic monographs. He has won eight science and technology awards, including first prizes in the Heilongjiang Provincial Technological Invention Award, the Achievement Award of the Creative Founder Award, and the China Machinery Industry Science and Technology Award.
Speech Title: Intelligent Space Robotic Operations: Technological Advances and Future Prospects
Abstract: Intelligent learning techniques hold considerable promise for future space operations, including space infrastructure construction and on-orbit servicing. They have the potential to address key challenges such as robots’ limited autonomous decision-making capabilities and adaptability to diverse task scenarios in complex space environments. This report focuses on AI-enabled space robotic operations and systematically outlines their fundamental concepts and technological framework. It provides a comprehensive overview of the key research issues and major technical challenges across three stages: before, during, and after training. It also explores future development trends, providing a technical foundation for the development of autonomous and intelligent space robotic systems for on-orbit operations.
Keynote Speaker V
Xiangpeng Xie
Nanjing University of Posts and Telecommunications, China
Brief Introduction: Xie Xiangpeng, professor and doctoral supervisor, senior member of IEEE/CAAI/CAA. His research focuses on intelligent perception and control of cyber-physical systems driven by physical layer network security authentication, as well as interpretability of artificial intelligence. He has been responsible for over 10 provincial and ministerial-level projects including the joint project of the National Natural Science Foundation of China, the Excellent Young Fund, and others. He has received the Young Science Award of the Chinese Association of Automation, the First Prize of Natural Science Award of the Chinese Automation Society, the Second Prize of Natural Science Award of Shanghai, the First Prize of Natural Science Award of the Chinese Simulation Society, the First Prize of Technological Progress of Jiangsu Digital Economy Society, the First Prize of China's Hundred Most Influential International Academic Papers, the Second Prize of Natural Science Outstanding Academic Works of Jiangsu, the First Prize of the National College Students' Extracurricular Academic and Technological Works Competition (first supervisor), and the Engineering Excellent Master's Thesis Award for Graduate Students of the Chinese Association of Automation. He serves as a member of the editorial board of six international journals including IEEE TII, IEEE TCYB, IEEE TFS, and a guest editor of IEEE TCE.
Speech Title: Physical layer network security authentication and rule-based explainable AI-driven human-machine shared driving system intelligent perception and control
Abstract: The sixth-generation mobile communication (6G) has accelerated the realization of the low-cost "integrated perception, computing, intelligence" ICPS physical scenario, bringing ICPS closer to the real world. It has gradually presented a new form of "human-machine integration, human-network integration", and in the future, ICPS will possess emerging features such as efficient independent decision-making, significant network security self-adaptability, and human-oriented value orientation. This report aims to explore the following research contents: proposing the "authentication to build trusted perception, control to eliminate authentication errors" hardware intrinsic signature characteristic technology (Signature of Hardware Emanation, SHE), ensuring that the networked control system moves from "default trustworthiness" to "trust explainability" and continuously exploring the mutual integration of communication, network security, and control across multiple disciplines. Through the combination of rules and learning, a new method of high-precision, generalized, and weak reasoning ability high-order self-organizing hierarchical fuzzy modeling has been established. On this basis, by integrating characteristic methods such as conditional variational autoencoder probability trajectory prediction and self-organizing Type-2 fuzzy inference engine, a lightweight, low-risk empirical knowledge embedding-based human-machine permission allocation solution has been proposed, gradually exploring effective ways to solve the problems that have plagued the intelligent driving field, such as the "dimension curse" and "rare event curse". Inspired by the "Ebbinghaus memory forgetting curve", a multi-sudden homogeneous polynomial enabling gain scheduling mechanism that can quantitatively describe the evolution trend of energy analysis functional has been proposed, enhancing the interpretability and adaptability of the core control algorithm. Finally, the team's preliminary application exploration in the directions of automotive active suspension and human-machine shared driving, as well as the achievements in application journals such as TIE, TMech, and TCST, are briefly introduced.