Speakers
Bingheng Luo
Bingheng Luo received a doctorate in mechanical manufacturing and automation from Xi'an Jiaotong University in 1986. He is currently the dean of the School of Mechanical Engineering of Xi'an Jiaotong University. He is also the leader of the Mechanical Discipline Evaluation Group of the Academic Degrees Committee of the State Council, an advisory member of the National Natural Science Foundation of China, the deputy chairman of the China Mechanical Engineering Society, and the deputy chairman of the China Machinery Manufacturing Technology Association. He has long been devoted to the research of advanced manufacturing technology, and has mainly carried out scientific research and teaching in additive manufacturing, biological manufacturing, micro-nano manufacturing and electronic manufacturing equipment. He developed the world's first ultraviolet rapid prototyping machine, which is an internationally advanced machine integrating optical, and electrical techniques with a series of rapid mold manufacturing technologies He has presided over "Nine-five" and "Ten-five" national key scientific and technological research projects and the National Natural Science Foundation of China, 973 topics and other major key projects. He published more than 300 papers and won more than 30 invention patents, utility model patents 3 items. In 2001, he was awarded the National May 1st Labor Medal and the outstanding contributor of the National “Nine-five” Plan. Won 1 Jiang's Science and Technology Achievement Award, 1 Ministry of Education Science and Technology Progress Award, 1 National Science and Technology Progress Award, 1 Shaanxi Science and Technology Award, 1 National Technology Invention Award.
Speech Title: Challenges and trends of the additive manufacturing (3D printing) technology in Industry 4.0
Speech Abstract: Additive manufacturing is one of the advanced manufacturing technology which has developed rapidly in recent years. It is adept at rapid and free-forming fabrication of 3D structures and widely used in new products development and personalization production. The development state of additive manufacturing systems are applications are introduced. The challenges and trends of in industry 4.0 are presented.
Henry X.G. Ming
Xinguo Ming is a professor of School of Mechanical Engineering, Shanghai Jiao Tong University, Director of International Data Space (IDS) China Research Lab, and Member of National Academy of Artificial Intelligence (NAAI) of USA. He was the Member of Expert Committee of the China Industrial Services Alliance, member of the Shanghai Information Technology Expert Committee, Director of shanghai Center for Promoting Information Technology and Industrialization Integration. He was a Research Scientist in Singapore Institute of Manufacturing Technology and Visiting Faculty Fellow in MIT. His research interests include Industrial Intelligent LLM, International Data Space (IDS), Data-Driven Smart Business Decision Making, Data-Factor Value-Chain Symbiosis, Enterprise Digital and Intelligent Transformation, Industrial Artificial Intelligence, Industrial Internet, Smart Manufacturing System, Smart Product Innovation Ecosystem, Smart Product Service Ecosystem etc. Prof. Ming has published over 190 scientific papers and 10 books (in Chinese). He was a member of the editorial board of Concurrent Engineering: Research and Applications, Business Process Management Journal, etc. He undertakes and participates in a number of Industrial-Academic-Research cooperative projects funded by national and Shanghai government.
Speech Title: Industrial Intelligent Large Models Empowering New Industrialization
Speech Abstract: Based on the laws of market economy development and the global journey toward high-quality development, this presentation interprets the essential connotation of new quality productive forces and one of the development trends of new industrialization, namely, the digital and intelligent transformation of enterprises. Guided by the global direction of the digital economy, a general framework for data-driven industrial value creation is constructed, encompassing five aspects of data analysis, five contents of data value density, and six domains of value creation. Combining the current stage of global artificial intelligence development, the new industrial development model of mass personalization, and the nine levels of intelligent function enhancement, a general framework and industrial cases for industrial artificial intelligence are established. In light of the characteristics of large AI models, a general approach, framework, and implementation path for industrial intelligent large models integrating knowledge graphs and large AI models are proposed. Based on this, solutions for industrial intelligent large models in scenarios such as R&D design and equipment maintenance are developed.
Jin Yuan
Shanghai University of Engineering Science. He completed postdoctoral research at Shanghai Jiao Tong University and the Norwegian University of Science and Technology. Dr. Yuan is the Deputy Director of the Artificial Intelligence Committee of the Chinese Society for Agricultural Machinery, a member of the Embedded Systems Committee of the Chinese Society of Instrumentation, and the Deputy Director of the Agricultural Informatization and Intelligent Equipment Committee of the Shandong Agricultural Engineering Society. He is also an expert member of the Tai’an Intelligent Manufacturing Industry Expert Committee and has served as an editorial board member for the Transactions of the Chinese Society for Agricultural Machinery (11th and 12th terms). He is on the editorial board of an SCI Q2 international journal and serves as a peer reviewer for the National Natural Science Foundation of China (NSFC) and an evaluation expert for major national R&D programs under China’s 13th and 14th Five-Year Plans. He is also a science and technology service expert for the "Sci-Tech China" initiative of the Chinese Society for Agricultural Machinery, a core R&D member at the Collaborative Innovation Center for Efficient Wheat and Maize Production in Shandong Province, and a designated Sci-Tech Commissioner of Shandong Province. Dr. Yuan has long been engaged in the research of intelligent agricultural machinery theory and equipment technology. His research interests include intelligent agricultural equipment, agricultural robotics, electromechanical control, machine learning, and artificial intelligence. He has received six national and provincial-level awards, including the Second Prize of the National Science and Technology Progress Award and the Young Scientist Award from the Chinese Society for Agricultural Machinery. He has led more than 20 major research projects, including two NSFC grants, key national R&D programs, the China Postdoctoral Science Foundation, major agricultural innovation projects in Shandong Province, and provincial key R&D programs. Dr. Yuan has published over 90 high-quality SCI/EI papers in the field of agricultural engineering, with more than 1,200 citations. He is the chief editor of two English academic monographs, has filed 138 patent applications (87 granted invention patents, 105 utility models), and holds 26 software copyrights. He has made significant original contributions to intelligent harvesting equipment, particularly in key technologies for low-loss and high-efficiency harvesting and intelligent agricultural machinery control.
Speech Title: Selective Harvesting Robots: R&D Practices and Emerging Trends Toward Embodied Intelligence
Speech Abstract: Selective harvesting is becoming a critical research frontier in agricultural robotics, driven by labor shortages, rising harvesting costs, and increasing demand for high-quality fresh produce. This presentation reviews recent progress in selective harvesting technologies for fruits, vegetables, edible fungi, medicinal plants, and tea, with a focus on efficiency improvement, low-damage manipulation, robust perception, and continuous field operation. It further introduces practical research on selective harvesting robots for white asparagus, clustered tomatoes, and multi-pass cotton harvesting. Key technical advances include high-speed low-damage end-effectors, deep-learning-based target recognition and localization, multi-view perception, cluster-level harvesting sequence planning, adaptive task scheduling, and ROS-based system integration. Field and laboratory trials demonstrate the feasibility of these robotic systems, while also exposing persistent challenges in occlusion handling, collision-free manipulation, reliability, maintenance, and cost-effectiveness. Finally, the presentation discusses future trends in agricultural robotics, emphasizing the integration of mechanical intelligence, embodied intelligence, Vision-Language-Action models, world models, multimodal perception, and adaptive policy learning. These developments point toward moderately autonomous, efficient, low-damage, and economically viable selective harvesting robots suited to diverse agricultural production systems.
Vishal S Sharma, Sr.
Vishal Santosh Sharma is an accomplished Senior Lecturer and Research Coordinator at the Engineering Institute of Technology, Melbourne, Australia, specializing in Mechanical Engineering. He holds a Doctorate degree from Kurukshetra University in India, followed by a post-doctoral fellowship at École Nationale Supérieure d'Arts et Métiers in France, one of the renowned Grandes Écoles. Subsequently, he actively participated in various projects related to wind turbines and machining at NTNU in Norway. With extensive experience of over 25 years in teaching and research, Vishal has made significant contributions to the field of advanced manufacturing. He has previously worked at the University of the Witwatersrand in Johannesburg, South Africa, as well as at Dr. B R Ambedkar NIT Jalandhar, India. Prior to his academic career, he spent three years working in the industry, and even while pursuing teaching and research, he maintained strong ties with the industrial sector, successfully completing multiple collaborative projects. Vishal’s research primarily focuses on advanced manufacturing, resulting in the publication of over 110 scientific articles. His work has garnered more than 4,500 citations, establishing an impressive H-index of 38 on Scopus. His expertise has been acknowledged globally, as he was recognized among the top 2% of researchers for three consecutive years worldwide, according to a study conducted by Stanford University. Furthermore, Vishal has demonstrated his leadership abilities by organizing seven international conferences, editing special journal issues, and publishing three books in collaboration with Springer publishers. As a sought-after supervisor, Vishal has successfully guided thirteen PhD and over 30 Masters students in their research journeys, fostering their academic growth. He has also forged productive collaborations with international faculty members and industry partners, further enriching his contributions to the field. Additionally, Vishal is serving as an Associate Editor for esteemed journals such as the Journal of Intelligent Manufacturing, Alexandria Engineering Journal, and International Journal of Mechatronics and Manufacturing Systems, further highlighting his influential contributions in academia.
Speech Title: Industry 4.0-Enabled Predictive Maintenance and Bottleneck Optimisation of a Flexible Manufacturing System Using Machine Learning
Speech Abstract: Flexible Manufacturing Systems are widely used to improve productivity, flexibility, and responsiveness in modern manufacturing. However, their performance can be affected by machine breakdowns, uneven workload distribution, bottlenecks, material-handling delays, and poor maintenance planning. This expert talk will discuss how an FMS can be analysed and improved by combining workload calculation, station utilisation, bottleneck identification, and maintenance planning. The talk will begin with a simple FMS example involving different product types, machining stations, load/unload operations, and material handling. It will then explain how basic performance measures such as workload per station, workload per server, utilisation, busy servers, throughput, downtime, and bottlenecks can be calculated. The discussion will further extend toward Industry 4.0 by showing how sensor data, machine condition monitoring, digital models, and machine learning can support predictive maintenance and better decision-making. The main focus will be on converting traditional FMS analysis into a smart manufacturing framework where machine health, production performance, and maintenance actions are linked together. The talk will highlight how predictive maintenance can reduce unexpected downtime, improve machine availability, support bottleneck reduction, and enhance overall manufacturing productivity. This topic is highly relevant for industries moving toward data-driven, reliable, and efficient manufacturing systems.
Pai Zheng
Pai Zheng (F’ASME, SM’IEEE, M’SME/HKIE, AM’CIRP) is currently an Associate Professor, Wong Tit-Shing Endowed Young Scholar in Smart Robotics, and Director, National Centre of Technology Innovation for Intelligent Design and Numerical Control (NCDC) – Hong Kong Branch, in the Department of Industrial and Systems Engineering, at The Hong Kong Polytechnic University (PolyU). Before joining PolyU, he has been a Research Fellow at Nanyang Technological University, Singapore (2018-2019). He received the Dual Bachelor’s Degrees in Mechanical Engineering (Major) and Computer Science and Engineering (Minor) from Huazhong University of Science and Technology, Wuhan, China, in 2010, the Master’s Degree in Mechanical Engineering from Beihang University, Beijing, China in 2013, and the Ph.D. degree in Mechanical Engineering at The University of Auckland, Auckland, New Zealand in 2017. His research interests include human-robot collaboration, smart product-service systems, and industrial AI. Dr Zheng is a recipient of the Clarivate High Cited Researchers (2025 - ), HKIE Young Engineer of the Year Award (2025), SME Outstanding Young Manufacturing Engineers Award (2024), NSFC Excellent Young Scientist Fund (2024), PolyU Young Innovative Researcher Award (2023), and Global Top 50 AI+X Chinese Scholars by Baidu (2022). He serves as the Chair of IEEE Hong Kong Section SMC Chapter, Co-chair of IEEE TC on DMHCA, Scientific Committee Member of SME | NAMRI, Associate Editor of Journal of Manufacturing Systems, IEEE Transactions on Automation Science and Engineering, Journal of Intelligent Manufacturing, and International Journal of Interactive Design & Manufacturing.
Speech Title: Towards Embodied Robotic Manufacturing Systems: A Human-in-the-Loop Vision-Language Model and Contact-Rich Learning Approach
Speech Abstract: Anchored in the ethos of Industry 5.0, Human-Robot Collaboration (HRC) enables overall system performance and human well-beings by embracing the cutting-edge Artificial Intelligence (AI) and digital technologies. In such context, Vision-Language Models (VLMs), as a typical type of foundation models, have garnered significant attention and are extensively applied in HRC tasks by achieving remarkable outcomes. This talk proposes a physics-informed and VLM-enhanced human-in-the-loop approach for cost-effective robotic learn-to-assembly in smart manufacturing, of which three main aspects yet to be explored coherently: 1) Multimodal Intelligence-based Human Demonstration (Perception/Cognition); 2) Cross-Domain Tool-use Skill Transfer (Behavior Learning), and 3) Foundation-model based NoCode Manufacturing System Execution (Physics-informed Execution). It is assumed that the success of this research endeavor could potentially pave the way for more natural HRC and effective robot learning and manipulation in production, as the next-generation Embodied Robotic Manufacturing Systems, characterized by enhanced flexible automation capabilities.
Yifan Liu
Dr. Yifan Liu is Chief Expert of CETC 21st Research Institute. He is a nationally recognized leading AI talent for central SOEs, recipient of multiple provincial and corporate honor titles, and a former visiting scholar at EPFL, Switzerland. He serves on expert panels for national and Shanghai municipal research planning and is a standing committee member of Shanghai Rehabilitation Medical Engineering Society. With over 15 years in robotic R&D, he previously took senior R&D management roles at SIASUN, EFORT and MicroPort Medical Robotics. His work pioneers domestic collaborative robot core technologies (covered by CCTV’s ‘Chao Ji Gong Cheng’) and world-leading 5G remote surgical robotics, realizing the world’s first 5500km remote robotic surgery.
Speech Title: Embodied Intelligence: AI for Robotics
Speech Abstract: Rapid advances in large language models and artificial intelligence are driving transformative progress across robotic development and embodied intelligence. This talk focuses on the integration of large-model algorithms with machine vision, force sensing, motion control and robotic dexterous manipulation. It elaborates how embodied AI empowers industrial, humanoid and medical robots to autonomously understand environments, generate intelligent planning and perform complex dexterous tasks. The presentation further introduces verified engineering practices, discussing existing challenges and future development trends of large-model-driven embodied robotics.
Bin Zhou
Mr. Bin Zhou received his Bachelor and Master of Science degrees in Automotive Engineering from Tsinghua University, Beijing, China. He currently serves as the Vice President of Fourier Group and previously held the position of President of the General Robotics Business Unit at Fourier. He has been recognized as a Pudong New Area Pearl Leading Talent. Early in his career, Mr. Zhou worked at National Instruments (NI), where he was responsible for market development and technical expansion of instrumentation solutions in Greater China. He has accumulated over 15 years of cross-functional experience in electromechanical equipment, robotics product development, marketing, sales, and business strategy, and is also a successful serial entrepreneur. His notable awards include First Prize in the inaugural Freescale Smart Car Competition, Silver Award in a national entrepreneurship competition, and First Prize in a virtual instrumentation competition. In addition to his industry leadership, Mr. Zhou holds professorial appointments at Shanghai University, Shanghai Normal University, and Changshu Institute of Technology, where he mentors graduate students and conducts research on intelligent robotics, embodied AI, and advanced manufacturing systems.
Speech Title: Embodied AI 2.0: A New Journey from Morphology, Spatial Intelligence, and Multimodal Dimensions
Speech Abstract: The next generation of embodied intelligence is moving beyond fixed assumptions about robot form and function. In industrial practice, the optimal morphological design for a given task is not always humanoid — instead, it should be driven by scenario-specific requirements. This talk discusses three key directions for Embodied AI 2.0. First, morphology: while humanoid robots offer flexibility for human-centered environments, future systems will diversify into task-optimized forms that balance dexterity, stability, and efficiency in real production lines. Second, spatial intelligence remains the central technical bottleneck. True industrial deployment demands significant advances in three areas: robust environment perception under dynamic and cluttered conditions; natural and reliable human-robot interaction; and high-level task planning that adapts to uncertainty. Current approaches still fall short of these requirements. Third, multimodal perception — particularly the sense of touch — is emerging as the most critical frontier. Vision and proprioception alone are insufficient for contact-rich operations such as cable insertion, surface finishing, or flexible material handling. Incorporating high-resolution tactile sensing opens new capabilities for assembly, quality inspection, and adaptive manipulation. Drawing on practical case studies from Fourier’s general-purpose robotics unit, this presentation shows how moving beyond humanoid-centric designs, upgrading spatial reasoning, and adding tactile modalities can deliver measurable improvements in cycle time, deployment cost, and system flexibility. These are not distant research topics but the next engineering frontier for industrial automation.
Qiyuan Wang
Qiyuan Wang received his Doctor of Engineering degree in Mechanical and Electronic Engineering from Shanghai University in 2011. He currently works as a Senior Engineer at the Intelligent Technology Development Department, Research and Development Center, Shanghai-FANUC Robotics Co., Ltd. His primary research interests cover intelligent robotic technologies and the development of robot application software.
Speech Title: Intelligent Robots Empowering Intelligent Manufacturing
Speech Abstract: This report first introduces the development history of factory automation production lines, which have gone through three generations. It introduces the characteristics of each generation of automation production lines and the progress points compared to the previous generation. Then, the intelligent application of robots, mainly the intelligent application of vision, was introduced. This report introduces robot vision from four aspects: visual positioning, visual guidance, visual measurement, and visual detection, and provides application examples. Finally, the production situation of FANUC's current intelligent unmanned factory was introduced and demonstrated.