Keynote Speech 1: Learning and Feedback in the Control of Uncertain Systems⇒
By Lei Guo (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)
Keynote Speech 2: A Paradigm Shift in Machine Learning: From Data to Data- Knowledge Environment⇒
By Witold Pedrycz (University of Alberta, Edmonton, Canada)
Keynote Speech 2: Advanced Technology and Innovative Application of Embodied Intelligence for Pan-vascular Interventional Surgery Robots⇒
By Shuxiang Guo (Southern University of Science and Technology, China)
Keynote Speech 3: Collaborative Innovation Technologies and Applications for Large and Small Models⇒
By C. L. Philip Chen (South China University of Technology, China)
Keynote Speech 4: Opportunities and Challenges for Medical Robotics in the Age of AI⇒
By Zengguang Hou (Institute of Automation, Chinese Academy of Sciences)
Keynote Speech 5: Embodied Intelligence and Its Impact on the Future Development of Artificial Intelligence⇒
By Fuchun Sun (Tsinghua University, China)
Keynote Speech 6: Adaptive Intelligent Robotic Control Using Fuzzy Deep, Broad and Reinforcement Learning Techniques⇒
By Ching-Chih Tsai (Chung Hsing University, China)
Keynote Speech 7: AI-Driven High-Definition Glasses-free 3D Light Field Display with Large-Viewing-Angle⇒
By Xinzhu Sang (Beijing University of Posts and Telecommunications, China)
Keynote Speaker 1: Prof. Lei Guo
Title: Learning and Feedback in the Control of Uncertain Systems
题目：不确定系统控制中的学习与反馈
Abstract: Learning and feedback are complementary mechanisms in dealing with uncertain dynamical systems. Learning plays a basic role in the design of control systems, and feedback makes it possible for a control system to perform well in an open environment with uncertainties. In this talk, some basic results will be presented when online learning is combined with feedback in the control of uncertain dynamical systems. We will first consider the celebrated self-tuning regulators (STR) in adaptive control of uncertain linear stochastic systems, where the STR is designed by combining the recursive least-squares estimator with the minimum variance controller. The convergence of this natural and seemingly simple adaptive system had actually been a longstanding open problem in control theory. Next, we will discuss the rationale and foundation behind the widespread successful applications of the well-known proportional-integral-derivative (PID) control for nonlinear uncertain systems and provide a new online learning-based design method. Finally, we will present some basic results on more fundamental problems concerning the maximum capability and limitations of the feedback mechanism in dealing with uncertain nonlinear systems. These results may offer useful implications for the design and analysis of more complicated control systems where AI is combined with online feedback control.
摘要：学习与反馈是处理不确定动态系统的两种互补机制。学习在控制系统设计中发挥着基础性作用，而反馈则使控制系统能在充满不确定性的开放环境中良好运行。本次报告将阐述当在线学习与反馈控制相结合时，针对不确定动态系统控制所取得的基础性研究成果。我们将首先探讨不确定线性随机系统自适应控制中著名的自校正调节器（STR）——该设计通过将递推最小二乘估计器与最小方差控制器相结合实现。这一自然且看似简单的自适应系统，其收敛性问题实则是控制理论中长期悬而未决的难题。其次，我们将剖析非线性不确定系统中广泛成功的比例-积分-微分（PID）控制背后的理论基础，并提出一种新型的基于在线学习的设计方法。最后，针对反馈机制处理不确定非线性系统的最大能力与根本性局限等更基础的问题，我们将展示若干基本结论。这些成果可为人工智能与在线反馈控制相结合的复杂控制系统设计与分析提供重要启示。
Bio: Lei GUO is a professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS). He is a Fellow of IEEE, Member of CAS, Fellow of the Academy of Sciences for the Developing World (TWAS), Foreign Member of the Royal Swedish Academy of Engineering Sciences, and Fellow of the International Federation of Automatic Control (IFAC). In 2014, he was awarded an honorary doctorate by the Royal Institute of Technology (KTH), Sweden. In 2019, he was awarded the Hendrik W. Bode Lecture Prize by the IEEE Control Systems Society "for fundamental and practical contributions to the field of adaptive control, system identification, adaptive signal processing, stochastic systems, and applied mathematics". His current research interests include adaptive (learning, filtering, control and games) theory of stochastic systems, control of uncertain nonlinear systems, game-based control systems, multi-agent complex systems, and man-machine integration systems, etc.
简介：郭雷，中国科学院系统科学研究所研究员，国际电气与电子工程师学会会士（IEEE Fellow）、中国科学院院士、发展中国家科学院（TWAS）院士、瑞典皇家工程科学院外籍院士、国际自动控制联合会（IFAC）会士。2014年获瑞典皇家理工学院（KTH）荣誉博士学位，2019年因"在自适应控制、系统辨识、自适应信号处理、随机系统及应用数学领域的根本性与实用性贡献"被IEEE控制系统学会授予亨德里克·W·博德讲座奖（Hendrik W. Bode Lecture Prize）。其主要研究方向包括：随机系统的自适应（学习、滤波、控制与博弈）理论、不确定非线性系统控制、基于博弈的控制系统、多智能体复杂系统及人机融合系统等。
Keynote Speaker 2: Prof. Witold Pedrycz
Title: A Paradigm Shift in Machine Learning: From Data to Data- Knowledge Environment
题目：机器学习范式的转变：从数据到数据-知识环境
Abstract: Machine Learning (and AI) are inherently data-driven. Data are a lifeblood of design methodologies and drive current commonly encountered development practices. The usage of data is behind spectacular successes of AI but also leads in some far-reaching failures. From the usage perspective in the ML learning environment, data and knowledge are evidently different. Data are numeric and precise. Knowledge is general and usually expressed at the higher level of abstraction bringing an aspect of information granularity. We carefully revisit the key and promising trends that just have recently emerged under the banner of physics-informed Machine Learning and neuro-symbolic paradigm of AI. Having this in mind, we introduce a general concept of knowledge landmarks (knowledge anchors). The landmarks provide a vehicle that along with data navigate the design of ML models. The role of knowledge is two-fold. First, it fills data gaps existing in the input space. Second, it serves as a regularization term that is essential in avoiding constructing physically infeasible models. In the design process, we elaborate on two fundamental issues, namely (i) we discuss an origin and construction of knowledge landmarks, and (ii) we present a realization of learning mechanisms in the presence of data and knowledge. Main ways of elicitation of knowledge landmarks are identified and discussed, in particular, we elaborate on techniques based on large and diversified amounts of data collected in the past and encapsulated in the form of prototypes (formed through clustering techniques). Along this line of knowledge acquisition, the relevance of the landmarks is quantified through their granularity giving rise to granular knowledge landmarks. Another alternative of building knowledge landmarks is based on their acquisition through LLMs; in this case they are qualitative and described as symbols. The constraints imposed on the data-based ML model are quantified through a collection of magnitude and change of magnitude landmarks. Once knowledge landmarks have been acquired, a unified data-knowledge environment is constructed. In virtue of the low number of knowledge landmarks, the knowledge regularization is formalized in the form of the Gaussian Process regression where the probabilistic information granules are included in the minimization of the augmented loss function. Detailed illustrative studies are showcased using rule-based architectures.
摘要：机器学习（及人工智能）本质上是数据驱动的。数据是设计方法论的生命线，并推动着当前普遍采用的开发实践。数据的使用既是人工智能取得惊人成功的关键，也导致了一些影响深远的失败。从机器学习环境的应用视角来看，数据与知识存在显著差异。数据是数值化的、精确的；而知识是概括性的，通常以更高层次的抽象形式呈现，体现了信息粒度的特性。我们系统梳理了近期在"物理信息机器学习"和"神经符号人工智能"两大范式下涌现的重要趋势。基于此，我们提出了"知识地标"（知识锚点）的核心概念。这些地标与数据协同作用，共同指导机器学习模型的设计。知识的价值体现在双重维度：其一，填补输入空间存在的数据空白；其二，作为正则化项防止构建物理不可行的模型。在设计过程中，我们重点解决两个根本问题：（1）探讨知识地标的来源与构建方法；（2）提出数据与知识双驱动下的学习机制实现方案。研究系统梳理了知识地标的主要获取途径：一方面基于历史海量异构数据，通过聚类技术形成原型进行知识封装，这种基于粒度量化的方法催生了"粒度化知识地标"；另一方面利用大语言模型获取定性表达的符号化地标。对于数据驱动的机器学习模型，我们通过"量级地标"和"量级变化地标"集合来实现约束量化。构建知识地标后，即可形成统一的数据-知识协同环境。鉴于知识地标数量有限，其正则化作用通过高斯过程回归实现——在增强损失函数的最小化过程中融入概率信息粒。最后，我们通过基于规则的架构开展了详细的案例验证研究。
Bio: Witold Pedrycz (IEEE Life Fellow) is Professor in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, Granular Computing, and Machine Learning. Professor Pedrycz serves as an Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of J. of Data Information and Management (Springer).
简介：维托尔德·佩德里茨（Witold Pedrycz，IEEE终身会士）现任加拿大埃德蒙顿阿尔伯塔大学电气与计算机工程系教授，同时兼任波兰华沙系统研究所研究员。佩德里茨博士是波兰科学院外籍院士、加拿大皇家学会会士，曾获多项国际学术殊荣，包括：IEEE系统、人与控制论学会颁发的诺伯特·维纳奖、IEEE加拿大计算机工程奖章、欧洲软计算中心卡哈斯特软计算奖、基拉姆奖、IEEE计算智能学会模糊系统先驱奖，以及2019年IEEE系统人与控制论学会杰出服务奖。其核心研究方向涵盖计算智能、粒计算与机器学习三大领域。佩德里茨教授现任《WIREs数据挖掘与知识发现》（Wiley）主编，并担任《数据信息与管理学报》（Springer）联合主编。
Keynote Speaker 3: Prof. Shuxiang Guo
Title: Advanced Technology and Innovative Application of Embodied Intelligence for Pan-vascular Interventional Surgery Robots
题目：泛血管介入手术机器人具身智能前沿技术与创新应用
Abstract: Remote-controlled vascular interventional robots (RVIRs) are being developed to increase the accuracy of surgical operations and reduce the number of occupational risks sustained by intervening physicians, such as radiation exposure and chronic neck/back pain. However, complex control of the RVIRs improves the doctor’s operation difficulty and reduces the operation efficiency. Furthermore, incomplete sterilization of the RVIRs will increase the risk of infection, or even cause medical accidents.
Our project aims at the development of a Panvascular Interventional Surgery Robot Systems with Force Feedback for Medical Applications. The results illustrated that the proposed RVIR has better performance compared with the previous prototype, and preliminarily demonstrated that the proposed RVIR has good safety and reliability and can be used in clinical surgeries. This presentation describes the basic research concepts and some new results in our big project.
摘要：血管介入手术机器人突破人手极限，灵活智能，末端器械采用智能设计原理，充分考虑自由度、刚度和操作力等因素，更有高效主从控制算法、神经网络学习算法的加入，能为手术操作精度保驾护航，实现精准、直观、智能的控制。
本报告重点就泛血管介入手术机器人技术和虚拟现实训练系统的开发现状，阐述血管介入手术机器人的前沿理论和关键技术，分析介入手术机器人在医疗临床领域的广泛应用前景。围绕泛血管介入手术机器人多器械协同控制机构、人机自然交互、5G远程遥操作、高精度力反馈等方面开展了从理论体系构建到关键技术攻关，再到创新应用落地的成体系研究。
Bio: Shuxiang Guo (Fellow, IEEE) is currently the Chair Professor with Southern University of Science and Technology, Shenzhen, China. He is also the Chair Professor with Beijing Institute of Technology, Beijing, China. Prof. Guo has a fellowship of the Engineering Academy of Japan.. His Ph.D. was obtained at the Nagoya University, Japan (1995). His current research interests include micro robotics and mechatronics, micro robotics system for minimal invasive surgery, micro catheter system, micro pump, and smart material (SMA, ICPF) based on actuators. He has published about 500 refereed journal and conference papers. He also received the Chang Jiang Professorship Award from Ministry of Education of China in 2005, and was offered Thousand-Elite-Project in China. He is the founding chair for IEEE International Conference on Mechatronics and Automation. And he is the editor in chief for International Journal of Mechatronics and Automation.
简介：郭书祥，南方科技大学讲席教授，北京理工大学特聘教授，日本工程院外籍院士，IEEE Fellow, 国家特聘专家，教育部长江学者教授。工业和信息化部融合医工系统与健康工程重点实验室主任。曾任职日本国立香川大学机电工程系近30年，为该大学终身教授。长期从事微机器人技术、血管检查微系统、医疗生物用遥控微操作系统等方面的研究工作。在世界上率先开发出直径 1mm的两种脑血管检查微系统，在生物医学工程领域，是爱思唯尔2020年- 2024年连续5年的中国高被引学者。拥有40余项机器人和微系统方面的发明专利，产学研同步推进，入选2022年“科创中国”创业就业先锋榜，获2023年中国产学研合作创新奖（个人）。
Keynote Speaker 4: Prof. C. L. Philip Chen
Title: Collaborative Innovation Technologies and Applications for Large and Small Models
题目：大小模型协同创新技术与应用
Abstract: With the advancement of large model technologies, numerous research institutions worldwide have engaged in competitions regarding model parameter scale and computing power, sparking critical reflections in academia about the current development path of artificial intelligence. On one hand, the research and application of large models still heavily rely on high-performance computing ecosystems dominated by foreign technologies, making it difficult to ensure autonomous security in large model development. On the other hand, most current industrial intelligent scenarios demonstrate a disproportionate relationship between the utilization of large model capabilities and the computational costs invested, highlighting the need for more efficient and lightweight models. Addressing these developmental challenges, this lecture will analyze future industrial trends in AI by examining current research progress in both large and small models, with a focus on the potential of hybrid development approaches combining different model scales.
摘要：随着大模型技术的发展，国内外大量的研究机构都开始进行大模型参数量竞赛与算力竞赛，这引发了学术界对当前人工智能发展道路的批判性思考。一方面，大模型研究与应用仍高度依赖以国外为主的高性能计算生态，对于大模型技术的自主安全难以把控。另一方面，当前大部分的工业智能化场景对于大模型性能的利用与付出的计算成本不成正比，反而需要更高效、更轻量的模型。针对这一发展问题，本次讲座将结合当前的大模型与小模型的研究现状，分析未来人工智能的大小模型混合发展对于未来产业发展的趋势。
Bio: C. L. Philip Chen is the Chair Professor and Dean of the School of Computer Science and Engineering, South China University of Technology. Prior to this position he worked in the US in two different universities as a tenured professor, department chair and associate dean, and in University of Macao as the dean. He is a Life Fellow of IEEE, Fellow of AAAS, IAPR, CAA, CAAI, and HKIE; a member of Academia Europaea (AE), a member of European Academy of Sciences and Arts (EASA), and a Full Foreign Member of Russia Academy of Engineering (FFM-RAE). He received the IEEE Norbert Wiener Award in 2018, for his contribution in systems and cybernetics, and machine learnings, the IEEE Joseph G. Wohl Outstanding Career award, Wu WenJun Outstanding Contribution award from Chinese AI Association, and 2016 Outstanding Electrical and Computer Engineers Award from his alma mater, Purdue University.
He is a highly cited researcher by Clarivate Analytics from 2018-2023 and is listed in Stanford University/Elsevier World’s Top 2% Scientists in “Lifetime Scientific Impact” and in “Annual Scientific Impact (951 world-rank in 2024)” Rankings since 2019. His current research interests include cybernetics, systems, and computational intelligence. For his contribution in these research areas, he received two times best transactions paper award from IEEE Transactions on Neural Networks and Learning Systems for his papers in 2014 and 2018 and received three-time Macau natural science award. In professional service, he was the Editor-in-Chief of the IEEE Transactions on Cybernetics, the Editor-in-Chief of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, the President of IEEE Systems, Man, and Cybernetics Society. Currently, he is the director of two Guangdong Key Labs, the director of a research lab funded by the Ministry of Education, a Vice President of Chinese Association of Automation, and Co-President of Guangdong AI Industrial Association.
简介：陈俊龙教授 （ C. L. Philip Chen），华南理工大学特聘讲席教授、博士生导师、计算机科学与工程学院院长，教育部健康智能与数字平行人工程中心主任，广东省人工智能产业协会联席会长。他是IEEE Life Fellow、AAAS Fellow、IAPR Fellow、欧洲科学院院士（Academia Europaea）、欧洲科学与艺术院院士、俄罗斯工程院外籍院士、中国自动化学会(CAA) 、中国人工智能学会（CAAI）及香港工程师学会 (HKIE) Fellow。目前陈教授任中国自动化学会副理事长，曾任两个IEEE 顶级期刊主编，获IEEE 诺伯特·维纳奖、IEEE约瑟夫·沃尔终身成就奖、吴文俊人工智能领域杰出贡献奖、美国普渡大学杰出电机计算机杰出校友奖等荣誉。连续6年被列为全球高被引科学家，斯坦福大学发布的全球前2%顶尖科学家榜单。在高排名学者（Highly Ranked Scholars™ ) 计算机类（Computer Science）近五年的影响力全国排名中名列第一，位列全球第8名。
陈教授主要从事计算智能系统，数据挖掘和知识发现，信息和视频索引、检索、系统、控制论和机器人的研究。他围绕智能系统与控制、计算智能、数据科学等科研方向开展研究工作超过40年，在该领域取得一系列学术创新性成果。他曾获中国自动化学会自然科学奖及广东省科技进步奖一等奖。
Keynote Speaker 5: Prof. Zengguang Hou
Title: Opportunities and Challenges for Medical Robotics in the Age of AI
题目：智能时代医疗机器人面临的机遇和挑战
Abstract: Recently, faced with the growing challenge of population aging as well as the complex issues in disease diagnosis and treatment, the rapid advancement of artificial intelligence (AI) and robotics has introduced innovative technological solutions for medical applications. The integration of "AI + healthcare" and "robotics + healthcare" is emerging as a new growth driver in the medical technology sector. Medical robotics and AI technologies are increasingly becoming part of our healthcare ecosystem, demonstrating vast potential for future development.
While AI has accelerated progress in robotics, robotics, in turn, extends the practical capabilities of AI. However, the application of medical robotics and AI still faces multiple challenges. For instance, achieving efficient, reliable, and safe AI-enabled interaction and control remains a critical hurdle in further advancement.
This presentation will explore cutting-edge topics, including multimodal biosignal acquisition and processing, human-robot interaction in medical settings, and clinical applications. Additionally, it will highlight recent technological breakthroughs and provide insights into future directions in the field.
摘要：我们面临日益严重的人口老龄化问题，并且还面临复杂疾病的诊断与治疗等挑战难题，近年来，人工智能和机器人技术快速发展，为疾病的诊断与治疗提供了新的技术手段，“AI+医疗”、“机器人+医疗”正在成为医疗技术行业新的增长点，医疗机器人和人工智能技术正在加快进入我们的健康生活，发展空间巨大。人工智能促进了机器人技术的发展，机器人也延申了人工智能的硬能力。但医疗机器人和人工智能技术的应用也面临诸多挑战，例如，高效、可靠、安全的人工智能交互与控制是阻碍其发展的一个重要挑战。本报告将结合多模态生物信号的获取、处理以及机器人的人机交互等热点问题和临床应用，阐述相关领域的技术进展，及对未来发展的思考与展望。
Bio: Zengguang Hou is a professor at the Institute of Automation, Chinese Academy of Sciences (CAS) and IEEE/CAA Fellow. He is a recipient of the National Science Fund for Distinguished Young Scholars and was selected for the National Ten Thousand Talents Program. He serves as Vice President of the Chinese Association of Automation (CAA), Director of the CCF Technical Committee on Intelligent Robotics, and Deputy Director of the Intelligent Rehabilitation Committee of the Chinese Association of Rehabilitation Medicine. Additionally, he holds the position of Vice President of the Asia-Pacific Neural Network Society (APNNS) and serves on the editorial boards of several prestigious journals, including IEEE Transactions on Cybernetics, Neural Networks, and IEEE Transactions on Neural Networks and Learning Systems. Prof. Hou has been honored with numerous awards, including the Second Prize of the National Natural Science Award, the First Prize of the Beijing Natural Science Award, and the Yang Jiachi Science and Technology Award. Internationally, he received the Dennis Gabor Award from the International Neural Network Society (INNS), recognizing his outstanding contributions to the field.
简介：侯增广，中国科学院自动化研究所研究员，博士生导师，国家杰出青年基金获得者、万人计划入选者、IEEE/CAA Fellow。担任中国自动化学会副理事长、CCF智能机器人专业委员会主任、中国康复医学会智能康复专业委员会副主任等，是亚太神经网络学会(APNNS)副理事长，还担任《IEEE Transactions on Cybernetics》、《Neural Networks》、《IEEE Transactions on Neural Networks and Learning Systems》等期刊编委。获国家自然科学二等奖、北京市自然科学一等奖、杨家墀科技奖等，还获得国际神经网络学会(INNS)丹尼斯·甘伯奖等。
Keynote Speaker 6: Prof. Fuchun Sun
Title: Embodied Intelligence and Its Impact on the Future Development of Artificial Intelligence
题目：具身智能及其对人工智能未来发展的影响
Abstract: Embodied intelligence refers to intelligent agents with physical entities that can acquire information, interact with environments, comprehend problems, make decisions, and execute actions to demonstrate adaptive intelligent behaviors. This report proposes the concepts of embodied agents and cyber-physical systems, and thoroughly investigates how the perceptual module of embodied agents facilitates environmental adaptation and scene understanding through knowledge-guided mechanisms and active behaviors. It analyzes how the behavioral module acquires generalizable skills via embodied reinforcement learning, and explores how the cognitive module coordinates knowledge guidance for perception and decision-making for behaviors through task-knowledge integration, while achieving knowledge growth and updating via dynamic residual mechanisms. Ultimately, the iterative interactions and co-evolution among perceptual, cognitive, and behavioral modules enable the emergence and adaptation of intelligent behaviors.
Furthermore, the report examines the implications of embodied intelligence for the future development of artificial intelligence. Key analyses include enhancing task execution capabilities through learning, mastering, and discovering underlying patterns, as well as designing evaluation frameworks and testing systems to assess embodied agents across diverse tasks and scenarios. The application of embodied intelligence in 3C (computer, communication, consumer electronics) production lines is discussed through manufacturing case studies. Finally, future research directions for embodied intelligence are outlined."
摘要：具身智能指具有物理实体的智能体，能够通过获取信息、与环境交互、理解问题、决策执行等环节展现适应性智能行为。本报告提出具身智能体与信息物理系统的概念体系，深入探究具身智能体如何通过感知模块的知识引导机制与主动行为实现环境适应与场景理解，分析行为模块如何通过具身强化学习获得可泛化技能，阐释认知模块如何通过任务-知识融合协调感知知识引导与行为决策，并借助动态残差机制实现知识增长与更新，最终通过感知-认知-行为模块的迭代交互与协同进化，实现智能行为的涌现与适应。
进一步地，报告探讨了具身智能对人工智能未来发展的启示：重点分析如何通过学习、掌握与发现底层规律提升任务执行能力，以及如何设计评估框架与测试系统以验证具身智能体在多样化任务与场景中的表现。通过制造业案例研究，论述具身智能在3C（计算机、通信、消费电子）产线的应用实践。最后展望具身智能的未来研究方向。
Bio: Fuchun Sun is a professor at the Department of Computer Science and Technology, Tsinghua University. He is a recipient of the National Science Fund for Distinguished Young Scholars and a Fellow of IEEE/CAAI/CAA. He serves as a member of Tsinghua University Academic Committee, Deputy Director of the Tenured Professor Committee of the Department of Computer Science and Technology, and Director of the Center for Intelligent Robots at Tsinghua University's Institute for Artificial Intelligence. Prof. Sun holds several key national and international academic leadership positions, including membership in the National Key R&D Program Robotics Expert Group, Vice President of the Chinese Association for Artificial Intelligence (CAAI), Executive Council Member of both the Chinese Association of Automation (CAA) and the Chinese Society for Cognitive Science, and Chair of the CCF Technical Committee on Intelligent Robotics. He is the Editor-in-Chief of Cognitive Computation and Systems and AI and Autonomous Systems, the Executive Editor-in-Chief of CAAI Artificial Intelligence, Associate/Area Editor for IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Fuzzy Systems, and International Journal of Control, Automation, and Systems (IJCAS) and Editorial Board Member for Robotics and Autonomous Systems and International Journal of Social Robotics.
简介：孙富春，清华大学计算机科学与技术系教授，博士生导师，IEEE/CAAI/CAA Fellow, 国家杰出青年基金获得者;兼任清华大学校学术委员会委员，计算机科学与技术系长聘教授委员会副主任，清华大学人工智能研究院智能机器人中心主任。兼任担任国家重点研发计划机器人总体专家组成员，中国人工智能学会副理事长，中国自动化学会和中国认知科学学会常务理事，中国计算机学会智能机器人专委会主任。兼任国际刊物《Cognitive Computation and Systems》，《AI and Autonomous Systems》主编，《CAAI Artificial Intelligence》执行主编，国际刊物《IEEE Trans. on Cognitive and Development Systems》，《IEEE Trans. on Fuzzy Systems》和《International Journal of Control, Automation, and Systems (IJCAS)》副主编或领域主编，刊物《Robots and Autonomous Systems》和《International Journal of Social Robots》编委。
Keynote Speaker 7: Prof. Ching-Chih Tsai
Title: Adaptive Intelligent Robotic Control Using Fuzzy Deep, Broad and Reinforcement Learning Techniques
报告题目：基于模糊深度/宽度/强化学习技术的自适应智能机器人控制
Abstract: Deep learning (DL) and reinforcement learning (RL) have been widely investigated and applied for many engineering applications. Broad learning systems (BLSs) have been shown to work as an effective and efficient incremental learning without the need for deep architecture, thus giving a new paradigm and learning system for AI systems. By incorporating with the merits of DL, RL, variant BLSs and fuzzy logics, this talk will present you fuzzy DL-based, BLS-based and RL-based control frameworks for autonomous mobile robots (AMRs) and multirobots. In the talk, some advances on fuzzy DL NN, fuzzy BLSs and fuzzy reinforcement learning systems are first mentioned, their applications to UAVs, wheeled AMRs and multirobots are discussed in some detail. Experimental results and videos are provided to illustrate the merits of the proposed fuzzy DL-based, BLS-based and RL-based control frameworks. Last but not least, some perspective topics on fuzzy deep, broad and reinforcement learning methods are recommended for future research.
摘要：深度学习（DL）与强化学习（RL）已在众多工程应用领域得到广泛研究和应用。而宽度学习系统（BLS）作为一种无需深度架构的高效增量学习方法，为人工智能系统提供了全新范式。本次报告将融合深度学习、强化学习、多样化宽度学习系统及模糊逻辑的优势，重点阐述基于模糊深度学习、宽度学习及强化学习的自主移动机器人（AMR）与多机器人系统控制框架。内容将涵盖模糊深度神经网络、模糊宽度学习系统及模糊强化学习系统的最新进展，并详细探讨其在无人机、轮式自主移动机器人及多机器人系统的应用。通过实验数据与视频演示，将直观展示所提出的三类模糊控制框架的优越性。最后针对模糊深度/宽度/强化学习方法的未来研究方向提出若干前瞻性课题建议。
Bio: Ching-Chih Tsai received the Diploma in the Department of Electrical Engineering from the National Taipei University of Technology, Taipei City, Taiwan Province, China, in 1981, the M.S. degree in the Institute of Control Engineering from National Chiao Tung University, Taiwan Province, China, in 1986, and the Ph. D degree in the Department of Electrical Engineering from Northwestern University, Evanston, IL, USA, in 1991. He is currently a Life Distinguished Professor in the Department of Electrical Engineering at National Chung Hsing University (NCHU), Taiwan Province, China. He served as Dean of College of Electrical Engineering and Computer Science, NCHU, since August, 2024. He has published and co-authored more than 700 technical articles and received many awards and recognitions from international conferences supported by IEEE. His current research interests include intelligent control, smart mobile robotics and automation intelligence with their applications to service and industrial robots, semiconductor manufacturing and AI-based control systems. He is a Fellow of IEEE, IET, CACS, RST, and TFSA.
简介：蔡清池教授于1981年在中国台湾省台北市的台北科技大学电机工程系获得学士学位，1986年在中国台湾省新竹市的交通大学控制工程研究所获得硕士学位，1991年在美国伊利诺伊州埃文斯顿的西北大学电机工程系获得博士学位。他目前是中国台湾省台中市的中兴大学电机工程系的终身杰出教授，担任中兴大学电机工程与计算机科学学院院长。他已发表和合著了700多篇技术文章，主要研究领域包括智能控制、智能移动机器人和自动化智能及其在服务机器人和工业机器人、半导体制造和基于人工智能的控制系统中的应用。他是IEEE、IET、CACS、RST和TFSA的会士。
Keynote Speaker 8: Prof. Xinzhu Sang
Title: AI-Driven High-Definition Glasses-free 3D Light Field Display with Large-Viewing-Angle
报告题目：AI驱动的大视角高清晰裸眼3D光场显示
Abstract: Glasses-free true 3D light field display delivers authentic 3D video experiences that closely mimic real-world scenes, which aligns with the natural way humans perceive the real world. With both physiological and psychological depth cues, it provides comprehensive 3D perceptual information, enabling more intuitive and holistic cognition. High-definition glasses-free 3D light field display with large-viewing-angle, requires breaking through limitations in the degree of freedom and precision of light control devices while balancing the spatial bandwidth product of display systems. This report presents our research on AI-driven high-definition glasses-free 3D light field display with large-viewing-angle based on AI-assisted design and calibration. A 65-inch 3D light field display is realized with a viewing angle exceeding 110 degrees, featuring accurate spatial geometric occlusion relationships. LED-based 54-inch and 162-inch 3D light field displays are demonstrated. AI introduces novel approaches to 3D light field video processing, significantly lowering the threshold for 3D light field content creation. Free-space 3D light field distributions is constructed, which enables aerial imaging-based dynamic glasses-free 3D light field displays.
摘要：裸眼真3D光场显示为观看者提供最接近真实场景的真3D视频，符合人们观看世界的真实感受，满足生理和心理的深度线索暗示，获取更完备的生理和心理的3D感知信息，实现更直观、全面的认知。实现大视角高清晰裸眼3D光场显示，需要突破控光器件控光自由度和精准度的限制，兼顾显示器件空间带宽积。报告介绍了研究团队在AI驱动的大视角高清晰裸眼3D光场显示方面的探索。通过AI辅助设计与校正，实现了65英寸的超过110度视角的3D光场显示，具有正确的空间几何位置遮挡关系；实现了基于LED的54英寸和162英寸的大视角的裸眼3D光场显示。AI为3D光场视频的处理提供了新思路，降低了3D光场内容产生的门槛。构建了自由空间3D光场分布，实现了多种形态空气成像的动态裸眼3D光场显示。
Bio: Xinzhu Sang is a Distinguished Professor at Beijing University of Posts and Telecommunications, and a recipient of the National Talent Program. He serves as a member of the Electronic Information Science and Technology Committee of the Ministry of Industry and Information Technology (MIIT), and the Secretary-General and Deputy Director of the Holography and Optical Information Processing Committee of the Chinese Optical Society. His research focuses on glasses-free 3D light-field display and novel photonic devices. He has presided over more than 30 major research projects, including Key Projects of the National Natural Science Foundation of China and the National Key Research and Development Program. He has published over 350 academic papers and won more than 100 invention patents. His research achievements have been featured in prominent exhibitions such as "The Great Reform – Celebrating the 40th Anniversary of Reform and Opening-Up Large-Scale Exhibition," "High-Tech Equipment Exhibition," and so on. He has been honored with "2017 Outstanding Teacher of Beijing" and "2016 Ethical Model Pioneer of Beijing." His won the 2023 Second Prize of the National Technology Invention Award, the 2022 First Prize of the Ministry of Education Technology Invention Award, the 2021 First Prize of the Beijing Science and Technology Award, and so on.
简介：桑新柱，北京邮电大学特聘教授，博士生导师，国家级人才计划入选者。兼任工信部电子信息科技委委员、中国光学学会理事、中国光学学会全息与光信息处理专委会秘书长和副主任等。主要从事裸眼3D光显示和新型光电子器件方面的研究，主持国家自然科学基金重点项目、国家重点研发计划等课题30余项，发表论文350余篇，获授权发明专利100余项，部分成果进行了应用转化。成果入选 “伟大的变革-庆祝改革开放40周年大型展览”、“中国科幻大会”等。被评为2017年北京市优秀教师和2016年北京市师德先锋等荣誉称号。荣获2023年度国家技术发明二等奖、2022年度教育部技术发明一等奖、2021年度北京市科学技术一等奖、2019年度教育部技术发明二等奖等。
