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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wang, K. Zhou, J. Li, S. Hu, J. Yang, J. Liu, M. Huan, Z. Ma, W. Yang, H. Zhang, S. Li, X. Sun, L. Soochow University Robotics and Microsystems Center School of Mechanical and Electric Engineering Jiangsu Province Suzhou City China University of Science and Technology of China Department of Precision Machinery and Precision Instrumentation Anhui province Hefei230026 China Xiamen University of Technology School of Electrical Engineering and Automation Fujian province Xiamen China

ISBN: (纸本)9781665436786

Multifunctional soft martials with good conductivity, flexibilities and stiffness tunable capabilities are key to developing soft robots. In this paper, we developed an innovative stiffness-tunable soft conducting elastomer by encapsulating liquid metal filled porous sponge with ecoflex. The elastomer exhibits good flexibility and conductivity in large scale of stretching, bending and twisting. Meanwhile, elastomer can enhance its stiffness by up to 8 times under external magnetic field. These characteristics of the elastomer provide great potential for future soft robots and actuators design. © 2021 IEEE.

关键词： Elastomers

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Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation

Employing Multi-Layer, Sensorised Kirigami Grippers for Sing...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Junbang Liang Joao Buzzatto Bryan Busby Ricardo V. Godoy Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering The New Dexterity research group The University of Auckland New Zealand The Information Technology R&D Center Mitsubishi Electric Corporation Japan The Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Soft-ware Engineering The Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Soft robotic devices have been popular in handling intricate grasping and dexterous manipulation tasks, serving as an alternative to conventional, rigid end-effectors. These devices are relatively simple, lightweight, and cost-effective. Recently, kirigami based structures have been used to create low-cost and disposable soft robotic grippers and hands. These grippers undergo a complex post-contact reconfiguration and conform to an object's shape and size during grasping. In this paper, we explore this new class of soft robotic grippers by utilising them for single-grasp object classification and grasping force estimation. We install simplistic sensors on both the gripper and the actuation system to estimate the state of the kirigami gripper, and the collected data features are employed to train Random Forest models for identifying the grasped object. The classifier trained exhibits a high accuracy of 98 % in discriminating objects of various shapes. When handling food items, the classifier achieves an accuracy of 94 %, while in classifying transparent objects, the classifier obtained again a high accuracy of 97 %. Finally, object-specific force estimation models are triggered based on the classification decision of the Random Forest model to estimate the grasping force exerted by the gripper. These positive outcomes demonstrate the kirigami based robotic gripper's potential for object classification in a variety of circumstances, particularly where vision systems are not available or not reliable.

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A Soft, Multi-Layer, Kirigami Inspired Robotic Gripper with a Compact, Compression-Based Actuation System

A Soft, Multi-Layer, Kirigami Inspired Robotic Gripper with ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Joao Buzzatto Junbang Liang Mojtaba Shahmohammadi Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering New Dexterity research group University of Auckland New Zealand Information Technology R&D Center Mitsubishi Electric Corporation Japan Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Software Engineering Centre for Automation and Robotic Engineering Science University of Auckland New Zealand

Over the last decade, a plethora of soft robotic devices have been proposed for the execution of complex grasping and dexterous manipulation tasks. Tasks requiring such increased dexterity are typically executed using fully-actuated, rigid end-effectors equipped with sophisticated sensing and controlled with complex control laws. The new class of soft robotic devices offers an alternative to the traditional end-effectors and facilitates the development of robotic grasping and manipulation solutions that are lightweight, safe to interact with, affordable, and easy to use and control. Within the class of soft robotic grippers and hands, promising recent developments were made in ultra-affordable, even disposable mechanisms based on origami and kirigami structures. This paper proposes a new kirigami-inspired robotic gripper geometry employing compression-based actuation. The compression actuation fundamentally differentiates this new design class from previous kirigami grippers, resulting in more compact robotic grippers with superior grasping capabilities. In particular, we investigate how the shapes of the internal cuts of the kirigami geometries can affect the gripper performance in terms of force exertion and grasping capabilities. A series of experiments are conducted to understand better the working principles behind this new type of kirigami grippers and experimentally validate their efficacy in the execution of complex, everyday life tasks. Further demonstrations of the gripper's capabilities include the pick-and-placing of human hair, egg yolk, and even liquids.

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Riemannian Online Optimistic Algorithms with Dynamic Regret

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IEEE Transactions on Automatic Control 2025年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong University of New South Wales School of Electrical Engineering & Telecommunications SydneyNSW2052 Australia Nanjing University of Science and Technology School of Automation Nanjing210094 China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Hong Kong University of Science and Technology Department of Mathematics Hong Kong Zhejiang University College of Control Science and Engineering Zhejiang 310058 China The University of Sydney Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering NSW2006 Australia

In this paper, we consider Riemannian online convex optimization with dynamic regret, which involves minimizing the cumulative loss difference between a learner's decisions and a sequence of adaptive decisions across dynamic environments over time on Riemannian manifolds. First, we propose Riemannian Online Optimistic Gradient Descent (R-OOGD), which extends the classical optimistic algorithms to Riemannian manifolds, requiring one gradient inquiry per round. We then propose Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD) by incorporating the meta-expert framework into R-OOGD. We analyze the dynamic regret of R-OOGD and R-AOOGD in terms of the regularity of the sequence of cost functions and comparators. The dynamic regret bound matches the results in Euclidean space and with a scale adjustment of O(1+κ D2/√1-KD2) related to the sectional curvature lower bound κ, sectional curvature upper bound and feasible set diameter. Finally, we provide numerical experiments to illustrate the performance of the proposed algorithms. © 1963-2012 IEEE.

关键词： Optimization algorithms

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Over-the-air Wireless Federated Learning Model for Generative AI

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IEEE Network 2025年

作者： Zheng, Jie Niyato, Dusit Zhang, Haijun Du, Hongyang Wang, Jiacheng Kang, Jiawen Xiong, Zehui Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi 710127 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China University of Hong Kong Department of electrical and electronic engineering Hong Kong Hong Kong Guangdong University of Technology Automation of School Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

Generative artificial intelligence (GenAI) technologies represent an important advancement in the field of AI, particularly for their capabilities in text and image generation. Over-the-air wireless federated learning (OA-WFL) can utilize the wireless waveform superposition property to achieve efficient model aggregation, thereby providing support for GenAI training and deployment. Therefore, this article investigates the support of OA-WFL for GenAI, focusing on potential applications and specific examples. We first discuss the OA-WFL and GenAI models, emphasizing their functionalities and the potential benefits arising from their interaction. We then explore its application in various GenAI scenarios, including large-scale edge device content generation, efficient distributed training of GenAI models, and reduction of bandwidth requirements and device load. Next, a framework is proposed to apply OA-WFL to diffusion models, such as those used in image generation, and validate its effectiveness through simulation results. Finally, we discuss prospective research directions for the application of OA-WFL for GenAI. © 2025 IEEE.

关键词： Federated learning

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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Cubic boron arsenide: An emerging semiconductor with exceptional thermal conductivity and high carrier mobility

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Matter 2025年第5期8卷

作者： Yue, Shuai Tian, Fei Song, Bai Zhong, Yangguang Bao, Jiming Liu, Xinfeng CAS Key Laboratory of Standardization and Measurement for Nanotechnology National Center for Nanoscience and Technology Beijing100190 China School of Materials Science and Engineering Sun Yat-sen University Guangdong Guangzhou510006 China Department of Energy and Resources Engineering and Department of Advanced Manufacturing and Robotics Peking University Beijing100871 China Department of Electrical and Computer Engineering and Texas Center for Superconductivity University of Houston HoustonTX77204 United States University of Chinese Academy of Sciences Beijing100049 China College of Materials Science and Engineering Hunan University Hunan Changsha410082 China

Over the past decade, cubic boron arsenide (BAs) has emerged as a highly promising semiconductor owing to its extraordinary thermal conductivity (1,200 W/m·K) and high ambipolar mobility (1,600 cm2/V·s). This unique combination has spurred significant research interest in its potential for advanced device applications. However, the challenge of growing uniform, high-quality crystals has hindered its widespread adoption, limiting its realization in high-performance devices. To accelerate the development and utilization of BAs, we systematically review recent theoretical and experimental advancements, focusing on key aspects such as carrier mobility, carrier relaxation, and the impact of defects on thermal conductivity and carrier mobility. Additionally, we explore emerging applications, persistent challenges, and future research directions. By addressing these critical issues, we aim to inspire further research and facilitate the development of next-generation electronic and optoelectronic devices based on BAs. © 2025 Elsevier Inc.

关键词： Carrier mobility

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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Chinese automation Congress (CAC)

作者： Jiazhuo Li Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Polish Academy of Sciences Systems Research Institute Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results.

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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Chinese automation Congress (CAC)

作者： Liu Hu Luefeng Chen Jie Hu Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan The Tokyo Institute of Technology Tokyo Japan

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.

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MRI-guided robot intervention—current state-of-the-art and new challenges

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Med-X 2023年第1期1卷 141-181页

作者： Shaoping Huang Chuqian Lou Ying Zhou Zhao He Xuejun Jin Yuan Feng Anzhu Gao Guang-Zhong Yang Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy(NERC-AMRT) Shanghai Jiao Tong UniversityShanghai 200240China Materials and Technology Center of Robotics EmpaDübendorf 8600Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China The Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Magnetic Resonance Imaging(MRI)is now a widely used modality for providing multimodal,high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing *** addition to its diagnostic potential,its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping,as well as integrated instrument localization,target recognition,and in situ,in vivo monitoring of the therapeutic *** of current applications include neurosurgery,breast biopsy,cardiovascular intervention,prostate biopsy and *** applications in targeted drug delivery and MRI-guided chemoembolization are also being *** promising progress has been made in recent years,there are still significant basic science research and engineering *** paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation,sensing,new materials,interventional instruments,and interactive/real-time *** future research directions and new clinical developments are also discussed.

关键词： MRI-guided robot intervention Actuation Sensing Materials Interventional instruments Interactive/realtime MRI

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