Kaken Researchers Wang Lihui 20750243

Leo Migdal

-Dec 6, 2025, 11:19 PM

Information User Guide FAQ News Terms of Use Attribution of KAKENHI {{coauthor.Related.last_modified}} Updated Natural and safe Human-to-Robot (H2R) object handover is a critical capability for effective Human–Robot Collaboration (HRC). However, learning a robust handover policy for this task is often hindered by the prohibitive cost of collecting physical robot demonstrations and the limitations of simplistic state representations that inadequately capture the complex dynamics... To address these challenges, a two-stage learning framework is proposed that synthesizes substantially augmented, synthetically diverse handover demonstrations without requiring a physical robot and subsequently learns a handover policy from a rich 4D spatiotemporal... First, an offline, physical robot-free data-generation pipeline is introduced that produces augmented and diverse handover demonstrations, thereby eliminating the need for costly physical data collection.

Second, a novel 4D spatiotemporal flow is defined as a comprehensive representation consisting of a skeletal kinematic flow that captures high-level motion dynamics and a geometric motion flow that characterizes fine-grained surface interactions. Finally, a diffusion-based policy conditioned on this spatiotemporal representation is developed to generate coherent and anticipatory robot actions. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines in task success, efficiency, and motion quality, thereby paving the way for safer and more intuitive collaborative robots. Spraying is a critical surface treatment process in intelligent manufacturing, and coating quality directly affects product performance. Therefore, efficient, accurate, and intelligent coating defect detection is an essential technique to ensure product reliability. The past decade has witnessed rapid progress in coating defect detection techniques.

However, most existing studies have focused on specific methods or application scenarios, and there is a lack of systematic reviews that provide a comprehensive overview of this particular research area. To fill this research gap, this paper systematically reviews recent advances in coating defect detection, which covers methods from physical property-based non-destructive testing to deep learning-based approaches. Their fundamental principles, applicability in intelligent manufacturing, and current research progress are examined, and key challenges and potential solutions are discussed. Furthermore, integration of advanced intelligent manufacturing technologies into coating defect detection systems is analyzed to enhance system-level digitalization, automation, and efficiency. Finally, future development trends are explored and analyzed, including collaborative perception, cross-modal fusion, and autonomous decision-making. It is expected that this review will help to advance and accelerate theoretical research and engineering applications in coating defect detection by providing researchers with a comprehensive understanding.

Industry 5.0 advocates human-centric smart manufacturing (HSM), with growing attention to proactive human-machine collaboration (HRC). Meanwhile, the rapid development of Multimodal large language models (MLLMs) and embodied intelligence is driving an unprecedented evolution. This work aims to leverage these opportunities to enhance robots’ learning and cognitive capabilities, enabling seamless and natural interaction. However, current research often overlooks human–robot symbiosis and lacks attention to specialized models and practical applications. This review adheres to a human-centric vision, taking language as the pivot to connect humans with large models. To our best knowledge, this is the first attempt to integrate HRC, MLLMs and embodied intelligence into a holistic view.

The review first introduces representative foundation models to provide a comprehensive summary of state-of-the-art methods in the ”Perception-Cognition-Actuation” loop. It then discusses pathways and platforms for efficient spatial skills learning, followed by an analysis of four key questions from the ”Why, How, What, Where” perspectives. Finally, it highlights future challenges and potential research directions. It is hoped that this work can help fill the research gap between HRC and MLLMs, offering a systematic pathway for developing human-centered collaborative systems and promoting further exploration and innovation in this exciting... The resources are available at: https://github.com/WuDuidi/MLLM-HRC-Survey. The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework.

Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and...

Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels. Calibration between robots and cameras is critical in automated robot vision systems. However, conventional manually conducted image-based calibration techniques are often limited by their accuracy sensitivity and poor adaptability to dynamic or unstructured environments. These approaches present challenges for ease of calibration and automatic deployment while being susceptible to rigid assumptions that degrade their performance. To close these limitations, this study proposes a data-efficient vision-driven approach for fast, accurate, and robust hand–eye camera calibration, and it aims to maximise the efficiency of robots in obtaining hand–eye calibration images without...

By analysing the previously captured images, the minimisation of the residual Jacobian matrix is utilised to predict the next optimal pose for robot calibration. A method to adjust the camera poses in dynamic environments is proposed to achieve efficient and robust hand–eye calibration. It requires fewer images, reduces dependence on manual expertise, and ensures repeatability. The proposed method is tested using experiments with actual industrial robots. The results demonstrate that our NBV strategy reduces rotational error by 8.8%, translational error by 26.4%, and the number of sampling frames by 25% compared to artificial sampling. The experimental results show that the average prediction time per frame is 3.26 seconds.

Brain Robotics for Human-Robot Collaboration During the past decades, electroencephalography (EEG) has been used to analyse and understand the behaviours of human brains. Despite the challenges in capturing EEG signals accurately and consistently, recent developments in analysing brain EEG signals show the potential in using the signals for communication with different manufacturing equipment such as robots for... Using human brainwaves for communication with a robot offers two major advantages: (1) it allows an operator to control a robot while performing a related task to co-work with the robot, which will have... For example, using mental commands to control the robot can overcome the difficulties that often accompany the usage of voice commands in a relatively noisy robotic environment. This seminar will first present a snapshot of AI history and the latest advancement on brain robotics and HRC.

In order to understand the new technology and its future potential in HRC assembly, examples of brainwave-driven robot control will be explained. This seminar will then project the future growth enabled by brain robotics, with the challenges to be identified. Lihui Wang is a Professor and Chair of Sustainable Manufacturing at KTH Royal Institute of Technology, Sweden. His research interests are presently focused on brain robotics, cyber-physical systems, real-time monitoring and control, predictive maintenance, human-robot collaborations, and adaptive manufacturing systems. Professor Wang is actively engaged in various professional activities. He is the Editor-in-Chief of International Journal of Manufacturing Research, Editor-in-Chief of Robotics and Computer-Integrated Manufacturing, and Editor-in-Chief of Journal of Manufacturing Systems.

He has published 9 books and authored in excess of 500 scientific publications. Professor Wang is a Fellow of Canadian Academy of Engineering, CIRP, SME and ASME. He is also a Professional Engineer in Canada, the President of North American Manufacturing Research Institution of SME, and the Chairman of Swedish Production Academy. Date and time: Nov 18 (Wed), 2020, 22:00 HKT (GMT+8) [Local time] サービス概要検索マニュアルよくある質問お知らせ利用規程科研費による研究の帰属 Database of Grants-in-Aid for Scientific Research(KAKEN) is a public database which includes information on adopted projects, assessment, and research achievements from the Grants-in-Aid for Scientific Research(KAKENHI) Program.

This system is hosted by the National Institute of Informatics (NII）in cooperation with MEXT and JSPS. Information User Guide FAQ News Terms of Use Attribution of KAKENHI To develop a variable focus lens with a large aperture, we chose the liquid-membrane-liquid (LML) structure and a stack piezo actuator was employed as a driving mechanism. An amplifier mechanism was built in side of the LML lens chamber, so as to satisfy the volume variation which occurs inside the system. The resonant frequency of the deformation plate was studied. A customized pre-tension device was built to improve the performance of the elastic membrane and increase its the response frequency.

A series of comparison experiments was conducted among different diameters of the LML lens and different pre-tension applied on the membrane. A response speed of 45 Hz sine wave signal was generated and applied on the piezo, and a stabilized response signal was measured and collected by an electric photo detector. Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Kaken Researchers Wang Lihui 20750243

People Also Search

Information User Guide FAQ News Terms Of Use Attribution Of

Second, A Novel 4D Spatiotemporal Flow Is Defined As A

However, Most Existing Studies Have Focused On Specific Methods Or

Industry 5.0 Advocates Human-centric Smart Manufacturing (HSM), With Growing Attention

The Review First Introduces Representative Foundation Models To Provide A