Prof. Jian Huang, Huazhong University of Science and Technology, China

National High-level Young Talents

Huang Jian, a full professor at Huazhong University of Science and Technology, serves as the head of the Department of Image Recognition and Intelligent Science at the School of Artificial Intelligence and Automation, and the director of the Hubei Provincial Key Laboratory of Brain-inspired Intelligent Systems. He has served as the chairman of IEEE CIS Wuhan Branch, the vice chairman of Wuhan Automation Society, and the vice director of the Intelligent Robot Professional Committee of Chinese Association for Artificial Intelligence. Formerly served as a visiting professor at Nagoya University in Japan, a visiting professor at UPEC in France, and a JSPS Invitation Fellow sponsored by the Japan Society for the Promotion of Science. Hosted over 20 national and provincial-level important scientific research projects, including National Key R&D Program projects, National Natural Science Foundation key projects, and Ministry of Science and Technology International Cooperation Key Special Projects. Published over 300 academic papers, obtained over 30 national invention patents. He is also serving as an editorial board member for international journals such as IEEE Transactions on Fuzzy Systems and IEEE Transactions on Automation Science and Engineering.

Speech Title: Wearable Robotics for Enhancing Human Abilities

Abstract: This study develops three innovative robotic systems for wearable assistance and human motion augmentation to enhance human mobility and reduce physical burden. The first design is a customized supernumerary robotic limb that assists users in grasping, walking, and sit-to-stand movements. Optimized through a multi-objective design framework, the system delivers stable auxiliary performance in daily activities and effectively alleviates physical fatigue during human movements. The second system adopts bio-inspired vibration isolation and elastic actuation for an active suspended backpack, which redistributes upper-body load from the shoulders to the pelvis. The design relieves shoulder strain and improves overall locomotion metabolic performance under diverse terrain conditions. The third wearable centaur robot integrates a transformable wheel-leg mechanism and a unified control framework combining admittance-based speed regulation and Bézier trajectory planning. It enables flexible switching between efficient wheeled locomotion and stable legged walking, achieving adaptive and robust terrain adaptability in complex environments. Collectively, these systems form an integrated technical paradigm and demonstrate the transformative potential of advanced wearable robotics in improving human motor performance, mitigating physical strain, and accommodating diverse real-world operational scenarios.

Prof. Mou Chen, Nanjing University of Aeronautics And Astronautics, China

National Science Fund for Distinguished Young Scholars

Prof. Mou Chen, an IEEE Fellow, IET Fellow and a CAA Fellow, serves as the Dean of the College of Automation Engineering at Nanjing University of Aeronautics and Astronautics. He was the recipient of the National Science Fund for Distinguished Young Scholars in 2018, was selected into the National “Hundred-Thousand-Ten Thousand Talents Project” in 2019, and was included in the “New Century Excellent Talents Support Program” of the Ministry of Education in 2011. Currently, he serves as an editorial board member of several SCI-indexed English journals, such as IEEE Trans. Cybernetic、IEEE/ASME Trans. Mechatronics, etc., and also serves as an editorial board member of Chinese journals including Science China: Information Sciences, Acta Aeronautica et Astronautica Sinica, Acta Automatica Sinica, Control Theory & Applications, etc. He has successively won the Second Prize of the National Natural Science Award (ranked second), the First Prize of the Jiangsu Provincial Science and Technology Award (ranked first), the First Jiangsu Provincial Outstanding Contribution Award for Young Scientists and Technologists, the 2 First Prize of Provincial and Ministerial Award (ranked first), and 2 Second Prizes of the National Defense Science and Technology Progress Award (ranked first). He has applied for and been authorized more than 50 invention patents. He has published 3 monographs in Chinese and English and has published more than 200 academic papers.

Speech Title:Key Technologies for Safe Control of Aircraft Swarms in Complex Low-Altitude Environments

Abstract:This report focuses on the development needs of new quality productive forces in the low-altitude economy, addresses the core challenges of ensuring safe and efficient operation of aircraft swarms in complex low-altitude environments, systematically reviews key technologies for safety control of single aircraft and swarm flight in the low-altitude domain, and provides an outlook on future development directions, aiming to support the large-scale safe application of low-altitude aircraft.

Prof. Weiming Hu, Institute of Automation, Chinese Academy of Sciences, China

National Science Fund for Distinguished Young Scholars

Hu Weiming, male, is a researcher at the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, a PhD supervisor, a faculty professor at the University of Chinese Academy of Sciences, and the head of the Video Content Security Research Team. He is a national-level talent under the Ministry of Human Resources’ Hundred, Thousand, and Ten Thousand Talents Project, a young and middle-aged expert with outstanding contributions, enjoys a special government allowance from the State Council, and is the chief expert of a key national 863 project. He won the second prize of the National Natural Science Award (2020) as the first major contributor.

Speech Title: Channel-Wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition

Abstract: Graph convolutional networks (GCNs) have been widely used and have achieved remarkable results in skeleton-based action recognition. We propose a channel-wise topology refinement-graph convolution (GC) to dynamically learn different topologies and effectively aggregate joint features in different channels for skeleton-based action recognition. Channel-wise topologies are modeled by learning a shared topology as a generic prior for all the channels and refining the topology using channel-specific correlations between joints. Our refinement method introduces very few extra parameters and significantly reduces the difficulty in modeling channel-wise topologies. Furthermore, we reformulate graph convolutions (GCs) into a unified form, and theoretically show that the channel-wise topology refinement-GC relaxes strict constraints of GCs and then has stronger representation capability. In order to model long-range joint dependencies and dynamically adjust channel-wise feature weights, we propose a channel-wise topology refinement squeeze-excitation transformer. Global information is aggregated in the temporal and spatial dimensions to capture correlations between distant joints. The channel-attention mechanism extracts channel-level statistics using global average pooling. The channel weights are generated using a fully connected layer to reinforce action-adaptive feature channels. 

Prof. lin Wang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Chief Scientist of the National Key R&D Program

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. Professor, Doctoral Supervisor; Chief Scientist of the National Key R&D Program; Young Top-notch Talent of the Guangdong Provincial Special Support Program; Director of the Shenzhen Engineering Research Center for Intelligent Lower-Limb Rehabilitation Assistive Devices.Her research mainly focuses on the application of embodied intelligent human-robot interaction systems for motor rehabilitation and enhancement in motor dysfunction caused by neuromuscular degenerative diseases.

Speech Title:Embodied Intelligence-Based Human-Robot Interaction Technology and Systems for Efficient Motor Rehabilitation

Prof. Lingyan Hu, Shanghai University of Engineering Science, China

Director of the Institute of Robotics and Artificial Intelligence

Hu Lingyan, Professor and Doctoral Supervisor at Shanghai University of Engineering Science, Outstanding Contributor to Innovation and Entrepreneurship Education in National Universities, Member of the Physical Medicine and Rehabilitation Branch of the Chinese Medical Association. She was selected for the Jiangxi Provincial "Hundred, Thousand, and Ten Thousand Talents" Program and serves as the head of the Jiangxi Provincial VR Industry Innovation Outstanding Talent Team — the "VR-Based Intelligent Cloud Rehabilitation System R&D Team."

With over 20 years of research experience in artificial intelligence, digital twins, and intelligent equipment in the medical and pharmaceutical fields, she has achieved fruitful results in these areas. She has presided over and completed 3 projects funded by the National Natural Science Foundation of China, led the Jiangxi Provincial VR Industry Outstanding Talent Team project on "VR-Based Intelligent Cloud Rehabilitation System R&D," and completed 9 other provincial- and ministerial-level projects, with a total accumulated research funding of over 6 million RMB. She has published more than 40 papers as first author or corresponding author in the fields of artificial intelligence, digital twins, and intelligent equipment, including 1 paper in a top-tier journal and nearly 20 SCI journal papers. She holds over 10 authorized invention patents, with technology transfer achievements exceeding 1.5 million RMB.

Speech Title: AI-Assisted Diagnosis of Pulmonary Diseases

Abstract: The research group led by Professor Hu Lingyan at Shanghai University of Engineering Science has achieved notable results in AI-assisted diagnosis of bronchial tuberculosis and its clinical application. Supported by the Key Science and Technology Innovation Project of the Jiangxi Provincial Health Commission, Professor Hu's team has successfully developed, based on machine learning and reinforcement learning, a prototype of an AI-assisted diagnostic system for bronchial tuberculosis under bronchoscopy, a cloud service platform for bronchial tuberculosis, and a multimodal AI-assisted diagnostic system for pulmonary diseases. The prototypes are currently undergoing clinical testing at Jiangxi Provincial Chest Hospital and Jingdezhen No. 5 Hospital.

Assoc. Prof. Le Song, Tianjin University, China

National Science Fund for Distinguished Young Scholars

Le Song, Associate Professor, Doctoral Supervisor and Assistant Dean at the School of Precision Instruments and Opto Electronics Engineering, Tianjin University. He is the Principal Investigator of the Soft Matter Mechanics and Intelligent Equipment Laboratory (SMILE Lab) and a core member of the Micro Nano Manufacturing Technology (MNMT) research team. He obtained his Doctor of Engineering degree in Test Measurement Technology and Instruments from Tianjin University in 2008. His long term research focuses on optical multi dimensional force tactile perception methods. He has presided over 3 projects supported by the National Natural Science Foundation of China, 1 sub project of the National Science and Technology Support Program, as well as numerous provincial ministerial and enterprise commissioned projects. As the first or corresponding author, he has published more than 40 papers in journals including Adv. Mater., IEEE ASME T Mech and Pattern Recogn., among which 3 are cover papers. He holds over 30 authorized invention patents. His research achievements have been reported by domestic and international media such as China Science Daily, Science and Technology Daily, EurekAlert and Physics.org. He serves as a reviewer for authoritative journals including IEEE T RO, IEEE ToH, Soft Robotics and Measurement, and is the Youth Editorial Board Member of Nanotechnology and Precision Engineering. Besides, he holds concurrent positions as committee member of multiple academic societies under the China Instrument and Control Society and other organizations, as well as standardization technical expert. He has won the First Class Tianjin Technical Invention Award, the First  and Second Class Science and Technology Progress Awards from the China Instrument and Control Society, and the Third Class Science and Technology Progress Award from the Chinese Society for Metrology and Test. He was selected into the “Beiyang Scholar Young Backbone Faculty” program of Tianjin University.

Speech Title:Optical Multi-Dimensional Force Sensing Technology for Dexterous Human-Machine Interaction

Abstract:Force-tactile sensing is a core supporting technology for enabling natural human-machine interaction, precise robotic manipulation, and dexterous operation of intelligent equipment. Traditional electrical force-tactile sensors generally suffer from limitations such as weak electromagnetic immunity and insufficient spatial resolution, making it difficult to satisfy the multi-dimensional and high-precision sensing requirements in complex interaction scenarios. Optical force-tactile sensing, with its inherent advantages of electromagnetic interference resistance, rich information dimensions, passive operation, and excellent biocompatibility, has emerged as a critical technical path to break through the above bottlenecks. This report systematically presents the research team's series of advances and typical applications in the field of optical multi-dimensional force sensing. Focusing on three representative application scenarios of human-machine interaction and intelligent equipment—precision grasping of industrial robots, force sensing in interventional medical procedures, and mechanical characterization of biological surfaces and interfaces—the report elaborates on the principle design and device implementation of key technologies, including compound-eye three-dimensional visuo-tactile sensing, fiber speckle-based multi-dimensional force detection, reconfigurable sensing with liquid-core waveguides, and visuo-tactile fusion multi-modal sensing. Accurate extraction of multi-dimensional force information is realized through optical image interpretation and signal processing methods. Finally, the report prospects the development trends and application potential of optical force-tactile sensing technology in fields such as new-generation natural human-machine interaction and precision intelligent equipment.